ML_project.ipynb

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   "outputs": [
    {
     "data": {
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       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from warnings import filterwarnings\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import statsmodels.api as sm\n",
    "import missingno as msno\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from scipy.stats import levene\n",
    "from scipy.stats import shapiro\n",
    "from scipy.stats.stats import pearsonr\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.model_selection import ShuffleSplit, GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn import model_selection\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cross_decomposition import PLSRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import ElasticNetCV\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBRegressor, XGBClassifier\n",
    "from lightgbm import LGBMRegressor, LGBMClassifier\n",
    "from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import tree\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Personality = pd.read_csv(\"C:/Users/nikhil/Downloads/psyc.csv\")\n",
    "Personality[\"Total\"] = Personality[\"openness\"] + Personality[\"neuroticism\"] + \\\n",
    "Personality[\"conscientiousness\"] + Personality[\"agreeableness\"] + Personality[\"extraversion\"]\n",
    "\n",
    "data = Personality.copy()\n",
    "\n",
    "dataV = Personality.copy() # for VISUALIZATION\n",
    "dataV[\"gender\"] = pd.Categorical(dataV[\"gender\"])\n",
    "dataV[\"openness\"] = pd.Categorical(dataV[\"openness\"])\n",
    "dataV[\"neuroticism\"] = pd.Categorical(dataV[\"neuroticism\"])\n",
    "dataV[\"conscientiousness\"] = pd.Categorical(dataV[\"conscientiousness\"])\n",
    "dataV[\"agreeableness\"] = pd.Categorical(dataV[\"agreeableness\"])\n",
    "dataV[\"extraversion\"] = pd.Categorical(dataV[\"extraversion\"])\n",
    "dataV[\"Personality\"] = pd.Categorical(dataV[\"Personality\"])\n",
    "\n",
    "df = data.select_dtypes(include=[\"float64\",\"int64\",\"int32\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INFORMATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(315, 9)\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Index(['gender', 'age', 'openness', 'neuroticism', 'conscientiousness',\n",
      "       'agreeableness', 'extraversion', 'Personality', 'Total'],\n",
      "      dtype='object')\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 315 entries, 0 to 314\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   gender             315 non-null    object\n",
      " 1   age                315 non-null    int64 \n",
      " 2   openness           315 non-null    int64 \n",
      " 3   neuroticism        315 non-null    int64 \n",
      " 4   conscientiousness  315 non-null    int64 \n",
      " 5   agreeableness      315 non-null    int64 \n",
      " 6   extraversion       315 non-null    int64 \n",
      " 7   Personality        315 non-null    object\n",
      " 8   Total              315 non-null    int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 22.3+ KB\n",
      "None\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "                   count       mean       std   min   25%   50%   75%   max\n",
      "age                315.0  20.244444  2.616811   5.0  18.0  20.0  22.0  28.0\n",
      "openness           315.0   4.850794  1.537211   1.0   4.0   5.0   6.0   8.0\n",
      "neuroticism        315.0   4.584127  1.818623   1.0   3.0   5.0   6.0   9.0\n",
      "conscientiousness  315.0   4.812698  1.786315   1.0   4.0   5.0   6.0   9.0\n",
      "agreeableness      315.0   4.844444  1.718555   1.0   4.0   5.0   6.0   8.0\n",
      "extraversion       315.0   4.926984  1.466527   1.0   4.0   5.0   6.0   8.0\n",
      "Total              315.0  24.019048  3.417772  16.0  22.0  24.0  26.0  35.0\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Male      159\n",
      "Female    156\n",
      "Name: gender, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "serious        153\n",
      "extraverted     77\n",
      "responsible     40\n",
      "lively          24\n",
      "dependable      21\n",
      "Name: Personality, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "                        age  openness  neuroticism  conscientiousness  \\\n",
      "age                1.000000 -0.089076     0.000015          -0.051491   \n",
      "openness          -0.089076  1.000000    -0.037076           0.004867   \n",
      "neuroticism        0.000015 -0.037076     1.000000           0.063195   \n",
      "conscientiousness -0.051491  0.004867     0.063195           1.000000   \n",
      "agreeableness     -0.009930 -0.046185    -0.163421          -0.111187   \n",
      "extraversion      -0.044296 -0.078308     0.005296          -0.115865   \n",
      "Total             -0.090968  0.375761     0.468561           0.452846   \n",
      "\n",
      "                   agreeableness  extraversion     Total  \n",
      "age                    -0.009930     -0.044296 -0.090968  \n",
      "openness               -0.046185     -0.078308  0.375761  \n",
      "neuroticism            -0.163421      0.005296  0.468561  \n",
      "conscientiousness      -0.111187     -0.115865  0.452846  \n",
      "agreeableness           1.000000      0.072560  0.368121  \n",
      "extraversion            0.072560      1.000000  0.372614  \n",
      "Total                   0.368121      0.372614  1.000000  \n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender  Personality\n",
      "Female  dependable     5.636364\n",
      "        extraverted    5.000000\n",
      "        lively         5.500000\n",
      "        responsible    4.428571\n",
      "        serious        4.607843\n",
      "Male    dependable     5.200000\n",
      "        extraverted    5.578947\n",
      "        lively         5.500000\n",
      "        responsible    4.500000\n",
      "        serious        4.637255\n",
      "Name: openness, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender  Personality\n",
      "Female  dependable     5.454545\n",
      "        extraverted    5.706897\n",
      "        lively         2.750000\n",
      "        responsible    4.214286\n",
      "        serious        3.901961\n",
      "Male    dependable     4.900000\n",
      "        extraverted    6.631579\n",
      "        lively         3.500000\n",
      "        responsible    4.333333\n",
      "        serious        4.225490\n",
      "Name: neuroticism, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender  Personality\n",
      "Female  dependable     5.636364\n",
      "        extraverted    4.362069\n",
      "        lively         4.375000\n",
      "        responsible    4.642857\n",
      "        serious        5.156863\n",
      "Male    dependable     4.600000\n",
      "        extraverted    3.736842\n",
      "        lively         5.687500\n",
      "        responsible    3.916667\n",
      "        serious        5.078431\n",
      "Name: conscientiousness, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender  Personality\n",
      "Female  dependable     5.000000\n",
      "        extraverted    4.965517\n",
      "        lively         5.000000\n",
      "        responsible    3.714286\n",
      "        serious        4.862745\n",
      "Male    dependable     4.700000\n",
      "        extraverted    4.684211\n",
      "        lively         5.000000\n",
      "        responsible    4.500000\n",
      "        serious        5.107843\n",
      "Name: agreeableness, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender  Personality\n",
      "Female  dependable     5.454545\n",
      "        extraverted    4.603448\n",
      "        lively         4.000000\n",
      "        responsible    4.000000\n",
      "        serious        5.764706\n",
      "Male    dependable     5.200000\n",
      "        extraverted    4.736842\n",
      "        lively         2.812500\n",
      "        responsible    4.416667\n",
      "        serious        5.362745\n",
      "Name: extraversion, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Personality  Total\n",
      "dependable   18       6.000000\n",
      "             21       4.000000\n",
      "             23       5.333333\n",
      "             24       5.000000\n",
      "             25       4.750000\n",
      "                        ...   \n",
      "serious      27       5.466667\n",
      "             28       5.444444\n",
      "             29       4.666667\n",
      "             30       7.000000\n",
      "             31       5.666667\n",
      "Name: openness, Length: 63, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Personality  Total\n",
      "dependable   18       2.000000\n",
      "             21       3.000000\n",
      "             23       3.333333\n",
      "             24       4.000000\n",
      "             25       4.750000\n",
      "                        ...   \n",
      "serious      27       4.533333\n",
      "             28       4.666667\n",
      "             29       5.333333\n",
      "             30       4.750000\n",
      "             31       5.666667\n",
      "Name: neuroticism, Length: 63, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Personality  Total\n",
      "dependable   18       2.000000\n",
      "             21       2.000000\n",
      "             23       4.666667\n",
      "             24       3.000000\n",
      "             25       6.500000\n",
      "                        ...   \n",
      "serious      27       5.866667\n",
      "             28       6.444444\n",
      "             29       6.333333\n",
      "             30       6.000000\n",
      "             31       7.000000\n",
      "Name: conscientiousness, Length: 63, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Personality  Total\n",
      "dependable   18       4.000000\n",
      "             21       6.000000\n",
      "             23       4.000000\n",
      "             24       6.500000\n",
      "             25       3.250000\n",
      "                        ...   \n",
      "serious      27       5.333333\n",
      "             28       6.000000\n",
      "             29       6.555556\n",
      "             30       6.500000\n",
      "             31       6.333333\n",
      "Name: agreeableness, Length: 63, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "Personality  Total\n",
      "dependable   18       4.000000\n",
      "             21       6.000000\n",
      "             23       5.666667\n",
      "             24       5.500000\n",
      "             25       5.750000\n",
      "                        ...   \n",
      "serious      27       5.800000\n",
      "             28       5.444444\n",
      "             29       6.111111\n",
      "             30       5.750000\n",
      "             31       6.333333\n",
      "Name: extraversion, Length: 63, dtype: float64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "gender               0\n",
      "age                  0\n",
      "openness             0\n",
      "neuroticism          0\n",
      "conscientiousness    0\n",
      "agreeableness        0\n",
      "extraversion         0\n",
      "Personality          0\n",
      "Total                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(\"----\"*30)\n",
    "print(data.columns)\n",
    "print(\"----\"*30)\n",
    "print(data.info())\n",
    "print(\"----\"*30)\n",
    "print(data.describe().T)\n",
    "print(\"----\"*30)\n",
    "print(data[\"gender\"].value_counts())\n",
    "print(\"----\"*30)\n",
    "print(data[\"Personality\"].value_counts())\n",
    "print(\"----\"*30)\n",
    "print(df.corr())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"gender\",\"Personality\"])[\"openness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"gender\",\"Personality\"])[\"neuroticism\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"gender\",\"Personality\"])[\"conscientiousness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"gender\",\"Personality\"])[\"agreeableness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"gender\",\"Personality\"])[\"extraversion\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"Personality\",\"Total\"])[\"openness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"Personality\",\"Total\"])[\"neuroticism\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"Personality\",\"Total\"])[\"conscientiousness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"Personality\",\"Total\"])[\"agreeableness\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.groupby([\"Personality\",\"Total\"])[\"extraversion\"].mean())\n",
    "print(\"----\"*30)\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"gender\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"Personality\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"openness\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"neuroticism\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"conscientiousness\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"agreeableness\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r5DrUvO91P3BPZj4w7HmWag579gPXDHGMK4HrmvfBZoCrI+KfhjjPqT0DMvM48CUWf3LOsBwBjizZ297LYvRGwbuAJzLz2JDneAfww8z8cWb+D/AA8Pv93qiR60DzJv/dwKHM/NSw5wGIiDdExPrm67XAO4HvDWuezPxYZm7KzAkWD32+kZl9/yvcq4hY13yTiOaw8A+BoX23PjN/BLwQEZc2F20FhvaNq2Xey5APVRvPA2+LiPObv3NbWXz/uy8jGbmIuBf4N+DSiDgSEbcNeaQrgVtY3Ds59e32a4c80wZgf0T8B4vnET+cmUP/2MYIGQcejYjvAN8C9mXm14Y80weAe5o/szcDfzvccX75D8A7WdxrGqpmL3cv8AQwx2Kf+j77YSQ/QiJJbRnJPTlJaouRk1SakZNUmpGTVJqRk1SakZNUmpGTVJqRk1Ta/wE1aOHLLaOOVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataV[\"extraversion\"].hist(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Total\",y=\"gender\",hue=\"Personality\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Total\",y=\"gender\",hue=\"Personality\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"openness\",y=\"Total\",hue=\"Personality\",data=dataV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IiAimT5/O5s2biY6O5scff+TYsWMAzJv1CR263uflT8sYk9f4o4//URHZ6uwKujKznUSkn4hsFJGNqYnM286cPUtU6ya0vKcZlSpV5r5uPflq9Ureenc84eHhREZGcvbsWX777TcAWrRoQenSpQGoUTOceZ9+woTXX+XnH3cQVqwY27d+T9MGdSl7VWkKFixI945tWPttHAChhUKJbBEJwK2338qBfQcAOHjkCDEPPUT9du14c9Ikdv7yS7bjFxF69uzJRx99xMmTJ9myaSNNmgX04xjGGD/IzkQsnvQf4F+AOn+/BjzgbkdVnQhMBIiIiPDJ8/ipffwXxcGk96fRMLJuuvXr168nLOzCLFsRdzRk2qxFfLXif4wY8hi9H3qY4iXcP20LEFow1DUcNSQkhOQkR6XroaNH82ifPkRHRbFmwwbGvHN50xvHxsbStm1bChcuTMvodhQs6Ot/YmNMoPNpi19Vj6hqsqqmAB8A9Xx5/ZyIvDOKD6dMRJ21YL7//nu3+x3cv4+rypaly3096dT9fnZs38ptNWux5tuNHD/xB8nJyXw6/3OaNIjI8np/nTpFhfLlAfh4/vxLxle8eHESEhJcrytWrEjFihUZPXo0Hbrem813aYwJJj5tDopIBVU95HzZAdie1f6B4MlBQ3lu1LPcfvvtpKSkcN1117F48eIM+234Zh2T359AwdCCFC0axpg3JlC2/NWMHv4Erbo8gKpyd/OmtG0VleX1nn3kEXoPHkypEiVoWr8+e/fvz3L/Pn36MGDAAIoUKcI333xDkSJF6NGjB8eOHeOGG6vl6r0bY/InryV+EfkEiATKiMh+YCQQKSLhOLp64oH+3rp+ThyKi+PMReuKFC7CuJfHZ6iJ06dPH/r06eN6HdOlOzFdumc4Z7eYaLrFZBy1Grc7zrXcqm0rWrVtBUCb5s1p0zxjv3z3LvfRvYvjRu3QJ59xre/UqROdOnVKt+/atWvp27ev2/dojDFeS/yq6q6fIfMhKsYj6tSpQ1hYGK+99hq/Hk249AHGmKBjd/7ymbi4uDSvLPEbYzKykg3GGBNkLPEbY0yQscRvjDFBxhK/McYEmXxzc7fnW56t9/b54+0ua/8Jr79K0bAwYvs/4tE4LmXVqlWMGzfO7bMFqebN+oQftm7mn/96JcO2YsWKcSpA5s81xviGtfiNMSbIWOLPhZdeeolq1arRuHFj9ux2FFP7LX4P/Xp2pUt0c3p2uoefftkNwEODR/Do0y/Q8O6u1GjchpXLVgKQnJzM2BfG0qVVF9o3a8+kmTMBWLNhA9G9etFz0CDqREfz8ON9XWUjVqz6kkbN6nFX9J3MnTvXFc+GDRto0KABtWrVomHDhuz59UKBt8MHD9Kna3vublqPF154we37GTt2LHXr1uX2229n5MiRnv/AjDEBId909fhaXFwcM2bMYPPmzSQlJVHj9prceltNRj0zhJFjxnLtdTew9fs4Hn/2eb6YNQmAvfsPsvazGeyO30fzrg/QsGlDFsxaQLESxZj1xSz+Pvc3faLvI6pRIwC27tzJ+kWLqFCuHM179mHDd99S8/ZaDHl6EHNmLOS6qtfz2FMXHn6++eabWbNmDQULFuTLL7/klVdH8+b7UwDYtnkTC75cQ+EiRejV4W7atGlDRMSFukHLli1j165dbNiwAVWlXbt2rF69mqZNm/ruQzXG+IQl/hxas2YNHTp0oGjRogA0a9Gac+fOsjnuO554+MELO/59YfrGTm1bUaBAAf5x/bVUvrYyu3/ZzbpV6/hp508sW7wMgDMnE/h1714KhYZS57bbqHT11QDUqF6Dfft/IyysGFWuuZbrr7sBgPvvv5+JEycC8Oeff9K7d2927dqFiJB45qzr2g2bRFLqSkcJ6Y4dO7J27doMiX/ZsmXUqlULgFOnTrFr1y5L/MbkQ5b4PSglRSleogRzl65yrbsh5IhrObUMc9rXivLPl/5J42aNAaj0h2Pbmg0bKFSokGvfkJAQkpKTs7z+c889R7NmzZg3bx7x8fE0apImaae/dIZYVJVnn32W/v0DqnySMcYLrI8/h5o2bcr8+fM5c+YMCQkJrPryC4oUKULlKtfyxWLH9IyqytYffnQdM3fxMlJSUvg1/jf2793PdTdcR+PIxsyYOoPz588DsGvPHhJPZz7J+z9uuJF9+38jPn4PAJ988olr259//kmlSpUAx9SQaX2z5itOnvyDs2fPMH/+fBo5u5NStWrVikmTJrlG+Bw4cICjR4/m8NMxxgSyfNPi/7/Ho9O1rtPaExLidn1q6zqtM2FXZ+t6tWvXplu3btSsWZNy5cpRo6aji+SVN//DiyOG8t7bb5B0/jw9Ylpw+603A3BNxatp3KY7fyUkMvKVkVxR+Ao69+jMgX0H6NSiE6pKhZKl+TiLyVcKFy7MuJfH0yO2G0WKFKFZVKSrHv+wYcPo3bs3o0ePpk2bNumOuy28NoP7x3Lk0EEe6NM7XTcPQMuWLdm5cycNGjQAHMM8P/roI8qVK5etz8MYk3fkm8TvDyNGjGDEiBEA7Nh3YVL0if/3qWs57ZdRVJMGvPOKY7RM6pdRgQIFeGL4Ezwx/AngwpdRk3r1aFLvwjw1Y/419sJ5Iu8iKvIugHTlohs0aMDPP//sen1f/8EAdOhyLx26XCiWWv2aMq7ltGP4Bw0axKBBg7Lz1o0xeZh19RhjTJCxFr+P/Hf8S/4OwRhjAGvxG2NM0LHEb4wxQcYSvzHGBBlL/MYYE2S8dnNXRCYB9wBHVbWGc11pYCZQFYgHuqqqm9H0l6/Y5Ga4H8UPRTNZn9WFC8euyWVEF8TvO8C3GzfTvUObS+/sYRPfeYN+jz5xWcdMmTKFjRs38k4WzxMYY/Iub7b4pwCtL1r3DLBcVW8Eljtf53t79x1g5rzP3G5LSkryyjVVlZSUFCa+M94r5zfG5F1eS/yquho4cdHq9sBU5/JUIMZb1/eFjz76iHr16hEeHs6oZ4aw9fs4OrS8k3Nnz3L6dCLtmjfmhx938dy/x7NuwybqtejEWxOnMW3mfAb2GkifTn2I7RxLYmIisZ1j6diiI/XbteOz5csBGPnaa0ycPt11vbGvv8y7778NwIT33qLVPVHpSijHx8dz00030atXL2rUqMFzQwdx7uxZOraOZNjjAwBYNHeWK+b+/fuT7Kz/M3nyZKpVq0a9evVYt26dLz9GY4yP+Xocf3lVPeRcPgyU9/H1PWbnzp3MnDmTdevWERoaSvdeD7Dn11+IbNGKt8aN4ezZM7Tt0Jlbb76Rfw0fzPj3pjBv2rsATJs5nx3bdjB/xXxKXVmKpKQk3p78NsWKF6Pw7j+I6t6d6KgoOt59N8+MGUO/Hj0AWLh4PjM+ms2q1SvYvWc3Sxctp2zlkq4SylWqVGHXrl1MnTqV+vXrs2Pfcb74bKGraNyvu37m80XzXTEPHDiQ6dOn06JFC0aOHElcXBwlS5akWbNmriqdxpj8x28PcKmqiohmtl1E+gH9AKpUqeKzuLJr+fLlxMXFUbduXQD+TDjFVVeV4eFBT9GtbUuuuOIKhr8wBjju9viGTRtS6spSgKNb5o1/v8HGbzdyhRbg0JEjHD1+nJrVq3PsxAkOHT3KgTPHKFmyJJUqVuaDSe/z1ZoVNL+7KQVDQ1wllKtUqcK1115L/fr13V7z23Wr2bFtiyvmM2fOUK5cOdavX09kZCRly5YFoFu3bulKPxhj8hdfJ/4jIlJBVQ+JSAUg0/KPqjoRmAgQERGR6ReEv6gqvXv3ZsyYMcCFWj3HjhzmTGIiSefPc+7cWSjk/vgiRYu4lhfPWcyJ308we9lsqp4KpUbz5pz9+28AYlq1Yv4XX3DgzzO0b9vRde3HBz5Br/tj09XqiY+PJywsLKugad+5Gx9MeDPd6vnz51/muzfG5GW+Hs65EOjtXO4NLPDx9T2mefPmzJ4921W6+OTJPzi4fx+jnn2KR596hns6dOL1MS8CULxYGAmJmZdaTkhI4KoyVxEaGsrq9ev57eBB17ZOd9/NnCVLWLxkIe3atAeg2Z1RfPzpdBITL11CObRgqKvk8x2NmrJsySLXvidOnGDv3r3ccccdfPXVV/z++++cP3+eWbNm5fLTMcYEMm8O5/wEiATKiMh+YCTwMvCpiDwI7AW6eup6p2JX+rQsc/Xq1Rk9ejQtW7YkJSWFZBWatbybgqEFuSemE8nJyfToEM3KtetpfEdtQgoUoO5dHenZNYZSJUukO1fbjm15uNfDtItsR71balDt+utd22658UZOJSZy9dUVKF/eEVtk0yh+3vUz0TEtKRga4iqhHOLmfXa+rxcdW93JLTVu59W33uPxp551xRwaGsqECROoX78+o0aNokGDBpQqVYrw8PBsfQbGmLzJa4lfVe/NZFNzb13T17p160a3bt2A9GWZwTFj1oyFX7i+jFLn3U3V5L5OruUrr7qSGZ/NANx/GX27cGGGL6R+Dw6g34MD0nX1AGzfvj3d6yHDn2fI8Oddr+9u14Ehj/TNcI3Y2FhiY2PdvU1jTD5jT+4aY0yQscRvjDFBxhK/McYEGUv8xhgTZCzxG2NMkLHEb4wxQSbfzLnbd357j55vXoz7apppFStWjFOnTnHw4EEG9+/P+PcnX/Z1NqzbwKT/TOK9j97LSZjGGHPZrMXvARUrVsxR0jfGGH+wxO8B8fHxtL+rCQD3tm/NLz/96NrWonMf4rZsJ/H0afo9+U8at+nOHS07s3zp8nTnSElJoVWDVhw/ccL1umarC6+NMcZTLPF7WOu2MSxd7ChBdOzIYQ4fOU6dmjV4+c2JRDa6g7WfzeCLWZMY++JYTqep31OgQAHadWrHzEWLAFj5zTfcdtNNlCld2i/vwxiTf1ni97DW97Rn2RJH8l66eAEd2rQAYPnqrxk34UPqtehEy86x/H3ubw4dOJTu2I73dmTGAseXxv/NmUOPDh18G7wxJijkm5u7gaL81RUodeWV/LTzB5YuXsAHLztml1SFGRPfoNo/rgMuFI77/djvrmMrVKpA2TJl+Orbb9m0bRsfjh3r+zdgjMn3rMXvBa3bxjDpvXdI+Osvbqt+EwB33dmQdyd/jKpjaoEd23a4PbZ35870HTaMmFat3FbbNMaY3Mo3Lf4PYhb4tCxzVlpGt+XlUSMY8PgQ17rhgwfw1MhXiLirIykpKZSrUtntEM7oZs0YOHw493fsmOs4jDHGnXyT+P3h1CnHRChVq1ZlwZdrXOvLlC3H1j2Hna8cX0ZFihRmwqsjXfukfhnVa1SPeo3qudZv++knatx8c7qa/MYY40mW+APIB29/wKxJn/Bf69s3xniRJf4A0vexvoy6P+MkKcYY40l2c9cYY4KMJX5jjAkylviNMSbIWOI3xpgg45ebuyISDyQAyUCSqkbk9pzHenTi2GUe437Uv8Mt0xfkJhy/i46O5vlX3ybhrz8ZGNsj3XDTVJGRkYwbN46IiFx//MaYPMSfLf5mqhruiaQfCFSVlJQUf4fhsmTJEkqULOnvMIwxAci6enIhPj6em266iV69etH+ria89+ZrdL2nBR1a3sk7r70CQOLp08T0fJi6d3WkdlQMsxZ8DkDziOaMfXEs7SLb0bV1V/bu2QvA3gMHuKdPHxq0b0/b2Fj2HTwIwONPDmT480/TpkNL6jYKZ9Fnjr9IDh06RNOmTQkPD6dGjRqsWeNo2VetWpU/TjjqACUnJzHs8QG0jWrI4P6xnD59mostW7aMBg0aULt2bbp06eJ6OM0Yk//4K/ErsExE4kSkn7sdRKSfiGwUkY3Hjl1uJ47v7Nq1i4EDB/L0yH9x5PAhZi5axpylK9mxbQsb13/NspXrqHB1Ob77ci6bVsynZbPGrmOLlyjOwlULue+B+xjz3BgAho4ezb0xMXyzYAFd77mHYf/+t2v/o0ePsGjOUj6aPIPRL78AwMcff0yrVq3YvHkzW7ZsITw8PEOMe379he49Y1m04muKFS/Ou+++m2778ePHGT16NF9++SWbNm0iIiKC119/3QufljEmEPgr8TdW1drA3cAjItL04h1UdaKqRqhqRNmyZX0fYTZde+211K9fn69Xr+LrNavodHczOkdHsfvXXezds5saN9/I8tXfMOKl11m7Po6SJYq7jm3ToY3r9+a4zQBs2LyZrm0c67u3a8c3cXGu/Vu3iqZAgQLcVO1mjh13fBnWrVuXyZMnM2rUKLZt20bx4hfOn+rqipWoXfcOAO7p0IW1a9em2/7tt9+yY8cOGjVqRHh4OFOnTmXv3r0e+4yMMYHFLzd3VfWA8/dREZkH1ANW+yOW3AoLCwMcffx9Bw6i6/29022/IeQI3y6dxdIVqxn16ts0a3wHI554GABBXPuJCJdyRaErXMupVT6bNm3K6tWr+eyzz+jTpw9PPvkkvXr1Snfcxee++LWq0qJFCz755JNLxmCMyft83uIXkTARKZ66DLQEtvs6Dk9rdGcz5n76MYmJjr7xI4cP8fvxYxw8fJSiRQpzX6e2PDmgD5u37XQd8/lCR3//5ws+J7xOOAB31KrF7CVLAPh08WIa1qmT5XX37t1L+fLl6du3Lw899BCbNm3KsM+hA/vZHPcdAEsWzKFx48bpttevX59169bxyy+/AJCYmMjPP/+cg0/BGJMX+KPFXx6Y52x1FgQ+VtWluT1p2elz/FqWuVHTZuze9TM9YqIBKBoWxsvj3+WHfbt4dvQ4CkgBQkML8taY51zH/HnyT9o3a0+hQoUY9944AMaOGMHAESN4a9IkypQuzbsvvZTldVetWsXYsWMJDQ2lWLFiTJs2LcM+193wDz6ZNonnhg7i+hur8fDDD6fbXrZsWaZMmcK9997LuXPnABg9ejTVqlXL0WdhjAlsPk/8qrobqOnr63pD1apV2b79wh8rPR/sT88H+6fb54YbitIispHb4x8c+CBPPfdUunVVKlVi8ZQpGfZ96/X0N2T3/LgfgN69e9O7d+8M+8fHx7Nj33GuLH0Vi1d+k25b0aJFAceXRqqoqCi+++47t3EaY/IXG85pjDFBxsoy+8nyjcv9HYIxJkhZi98YY4KMJX5jjAkylviNMSbIWOI3xpggk29u7q4Y/w0rPHKmXQB0HdzEI2fLzIypMyhcpDAxXWO8eh1jjLlYvkn8eUlSUhLde3f3dxjGmCBliT8XEhMT6dq1K/v37+f02b8Z8PiTVKl6Pa/+6zlOJyZSqnRppo8fSYXyZWnRuQ81q9/M199tomv7aPadPkPRsKI8MPABdm7fyahhozh75izVKl7DhJde4sqSJYnu1YvRw4ZRu0YNfj/xO63uacbGr7fy4087GfTUo5w//zcFQoQ5c+Zw4403+vvjMMbkEdbHnwtLly6lYsWKbNmyhQVfrqFxZHP+PfJZ3nhvErOWLKdj1/sY+cqbrv3/Pn+erz//lMED+qQ7zzOPPcOQfw5hwcoFVK9WjZcnTMjyulOnT6bvA/1ZsXQNGzdupHLlyt54e8aYfMpa/Llw2223MWTIEJ5++mlq1G1MiVKl2PXTTh7q0RmAlOQUqpQv5dq/c7vWGc6R8FcCCX8lUK9hPQDui4mh9+DBWV43onZd3nz7dQ4dOkivB3pYa98Yc1ks8edCtWrV2LRpE0uWLOHNcWO4o2ET/lHtZj6e/7lrn7SF48KKFrms8xcMCXFN53ju3FnX+k4xXagTHsH/ViwjOjqa999/n6ioqFy+G2NMsLCunlw4ePAgRYsW5f777ye2/6Ns3RzHid+Pu0ognz9/nh0//ZLlOYqXKE6JkiXY+O1GAGYsXEijunUBR8G2zT/8AMCizxa6jonfG8+111al7wP9ad++PVu3bvXG2zPG5FP5psUfNbiBz8syb9u2jaFDh1KgQAGSVHj+pbGEFAxhzMjhJCQkkJyUxJN976X6Tf/I8jxj3hrjurl7Y8VrXKWYH3/gAXo/8QRTZs0i6q5o1/4LF89j9txPKRhakMrXVGL48OHZjtkYY/JN4veHVq1a0apVKwB27DvuWj9t9iLXcuqX0f9mT0l37KNDH3Ut31LjFmYumQmk/zKqdv31fLPAMan6mbCreXboPwF4/JEnePyRJwAod00pz7wZY0zQsK4eY4wJMpb4jTEmyOTZxK96YcJx4zvq+OD9HYYxJhfybOLff/Isf59OsOTvQ6rKn3//TfKhQ/4OxRiTC3n25u7kb34jFqhcqjCOedshWf5yu+/xAu6/386czrju/BWJbvf9/VTRLOM5/Mcpt+s9EVNO48osJjl1LNNj3Dl7+LBjQZXkQ4c4Pf3jyzreGBNY8mziTziXzFur9qRbN6/4WLf7DriyhNv1/56V8e1vq/uUmz3h0dfaZhnP/UOnuV3viZhyGldmMcWN7ZXpMe581X/AZe1vjAlsfunqEZHWIvKTiPwiIs/4IwZjjAlWPk/8IhICTADuBqoD94pIdV/HYYwxwcofLf56wC+qultV/wZmAO39EIcxxgQl8fWoGBHpDLRW1Yecr3sCd6jqoxft1w/o53x5E/CTh0IoAxy/5F6+ZTFlj8WUfYEYl8WUPZ6M6VpVLXvxyoC9uauqE4GJnj6viGxU1QhPnzc3LKbssZiyLxDjspiyxxcx+aOr5wBwTZrXlZ3rjDHG+IA/Ev93wI0icp2IFAK6AwsvcYwxxhgP8XlXj6omicijwBdACDBJVX/wYQge7z7yAIspeyym7AvEuCym7PF6TD6/uWuMMca/8mytHmOMMTljid8YY4JMUCR+EZkkIkdFZLu/Y0klIteIyEoR2SEiP4jIIH/HBCAihUVkg4hsccb1gr9jAscT3yLyvYgs9ncsqUQkXkS2ichmEdno73gARKSUiMwWkR9FZKeINPBzPDc5P5/Un79EZLA/Y3LG9YTzv+/tIvKJiBT2d0wAIjLIGdMP3vycgqKPX0SaAqeAaapaw9/xAIhIBaCCqm4SkeJAHBCjqjv8HJcAYap6SkRCgbXAIFX91s9xPQlEACVU9R5/xpJKROKBCFUNmAeARGQqsEZV/+scNVdUVU/6OSzAVa7lAI4HNvf6MY5KOP67rq6qZ0TkU2CJqk7xV0zOuGrgqGRQD/gbWAoMUNVfPH2toGjxq+pq4IS/40hLVQ+p6ibncgKwE6jk36hAHVLrOYc6f/zaOhCRykAb4L/+jCPQiUhJoCnwIYCq/h0oSd+pOfCrP5N+GgWBIiJSECgKHPRzPAC3AOtV9bSqJgFfAR29caGgSPyBTkSqArWA9X4OBXB1q2wGjgL/U1V/xzUeGAak+DmOiymwTETinCVG/O064Bgw2dkt9l8RCfN3UGl0Bz7xdxCqegAYB/wGHAL+VNVl/o0KgO1AExG5SkSKAtGkf9jVYyzx+5mIFAPmAINV1f2sLT6mqsmqGo7jqep6zj9B/UJE7gGOqmqcv2LIQmNVrY2j0uwjzi5FfyoI1Ab+o6q1gEQgIMqeO7ud2gGzAiCWK3EUhrwOqAiEicj9/o0KVHUn8AqwDEc3z2Yg2RvXssTvR84+9DnAdFWd6+94LubsJlgJtPZjGI2Ads7+9BlAlIh85Md4XJwtR1T1KDAPR9+sP+0H9qf5C202ji+CQHA3sElVj/g7EOAuYI+qHlPV88BcoKGfYwJAVT9U1Tqq2hT4A/jZG9exxO8nzpuoHwI7VfV1f8eTSkTKikgp53IRoAXwo7/iUdVnVbWyqlbF0VWwQlX93joTkTDnTXmc3Sktcfyp7jeqehjYJyI3OVc1B/w6WCCNewmAbh6n34D6IlLU+f9hcxz32PxORMo5f1fB0b/vlXlOA7Y6pyeJyCdAJFBGRPYDI1X1Q/9GRSOgJ7DN2Z8OMFxVl/gvJAAqAFOdIzAKAJ+qasAMoQwg5YF5jrxBQeBjVV3q35AAeAyY7uxa2Q3E+jme1C/GFkB/f8cCoKrrRWQ2sAlIAr4ncEo3zBGRq4DzwCPeujkfFMM5jTHGXGBdPcYYE2Qs8RtjTJCxxG+MMUHGEr8xxgQZS/zGGBNkLPEbkwsiEi4i0WletxORLJ+WFZGvvR+ZMZmz4ZzGXIKIFHQWzXK3rQ+OCp2P+jYqY3LOWvwmXxGRqs469B84a5ovE5EiInKDiCx1FlRbIyI3O/efIiKd0xx/yvk70rnfQmCHc56Cyc76+9+LSDPnQ1IvAt2ctea7iUgfEXnHeY7yIjJPHHMbbBGRhhddo4KIrHYeu11EmqRuF5Gxzvi/FJF6IrJKRHaLSDuffqAmX7LEb/KjG4EJqnorcBLohOPJzMdUtQ7wFPBuNs5TG8dcBNWAR3BUrb4NR/mBqTj+/3kemKmq4ao686Lj3wK+UtWaznP9cNH2+4AvnAXxauIoygUQhqM0xa1AAjAax5OvHXB80RiTK0FRssEEnT2qutm5HAdUxVGEa5azxALAFdk4zwZV3eNcbgy8DaCqP4rIXqDaJY6PAno5j0kG/rxo+3fAJGexvvlpYk6dhANgG3BOVc+LyDbnezEmV6zFb/Kjc2mWk4HSwElnqzz15xbn9iSc/x+ISAGgUJpjE70ZpHOCoKY4ZqWaIiK9nJvO64Wbbyk434+qpmCNNeMBlvhNMPgL2CMiXcBRGVVEajq3xQN1nMvtcMw45s4aoIfz+GpAFeAnHF0xxTM5ZjnwsPOYEOcMWS4ici1wRFU/wDG7WKCUUDb5nCV+Eyx6AA+KyBYcfe3tnes/AO50rm9A5q38d4ECzu6WmUAfVT2HY76C6qk3dy86ZhDQzHlMHFD9ou2RwBYR+R7oBryZmzdoTHbZcE5jjAky1uI3xpggY4nfGGOCjCV+Y4wJMpb4jTEmyFjiN8aYIGOJ3xhjgowlfmOMCTL/D+zUjWV2JwvDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"neuroticism\",y=\"Total\",hue=\"Personality\",data=dataV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06Xw3Zw4dw8MZ/8knqcbZpnU7atasyeeff87mzZsJDw/n999/5/DhwwDMnfkl7Tt38/C7ZYzJarzRxz9IRLY4u4KuT20lEeknIhtFZGNiIvO0M2fPEtayIc3vaUzJkqXo1qUHa9au4t33x1O1alUaNWrE2bNn+eeffwBo1qwZBQsWBKBylarMnfElE996nT9/305Injxs2/ILoXVrUaRQQYKCgujaoTXf/RgJQHCOYBo1awTA7Xfezr69+wDYf/AgEQ89RJ22bXln8mR2/PVXuuMXEXr06MFnn33G8ePH+XXTRho29unbMYwxXpDZFcE+AF4G1Pn7TeABdyuq6iRgEkDNmjUzpWZrYh//JXEw+aNp1GtUK1n7Tz/9REhIiOt5zbvqMW3mQtas/IaRQx+l10MPkzdf6vP1BgcFu4ajBgYGEh/nqHQ9fPRoBvXuTXhYGOs2bGDse1c2vXGfPn1o06YNOXPmpHl4W4KCrOibMSa5TD3jV9WDqhqvqgnAx0DtzDz+1Wh0dxifTJmEOuuF//LLL27X2x+9l0JFinBvtx507Ho/27dt4Y4q1Vj340aOHD1GfHw8M+Z9TcO6NdM83n+nTlG8aFEAvpg377Lx5c2bl5MnT7qelyhRghIlSjB69Gjad74vna/SGONPMvV0UESKq2qM82l7YFta6/uCJx4bznMvPMOdd95JQkICZcuWZdGiRSnW2/DDej79aCJBwUHkzh3C2LcnUqRoMUaPeJwW9z6AqtKqSShtWoSlebxnHnmEXkOGUCBfPkLr1GFPdHSa6/fu3ZsBAwaQK1cufvjhB3LlykX37t05fPgw5cpXuKbXbozJnjyW+EXkS6ARUFhEooFRQCMRqYqjqycK6O+p41+NmMhIzlzSlitnLt54dXyKmji9e/emd+/erucR93Yl4t6uKfbZJSKcLhEpR61G7op0PW7RpgUt2rQAoHWTJrRukrJfvuu93eh6r+NC7fAnnna1d+zYkY4dOyZb97vvvqNv375uX6Mxxngs8auqu36G1IeomAxRo0YNQkJCePPNN/n70MnLb2CM8Tt25S+biYyMTPLMEr8xJiUr2WCMMX7GEr8xxvgZS/zGGONnLPEbY4yfyTYXd2sMn5bG0sg0lrn39eC2V7T+xLdeJ3dICH36P3LFx7oWq1ev5o033nB7b0GiuTO/5Lctm3n25ddSLCtUvhb/7vzZkyEaY3yMnfEbY4yfscR/DcaMGUOFChVo0KABu3c5iqn9E7Wbfj06c294E3p0vIc//toFwENDRjLoqRep16ozlRu0ZtXyVQDEx8cz7sVx3NviXto1bsfkr74CYN2GDYT37EmPxx6jRng4Dw/u6yobsXL1t9RvXJum4XczZ84cVzwbNmygbt26VKtWjXr16rH774sF3g7s30/vzu1oFVqb0W+97/b1fDLxE1ccE16fkPFvmDHGJ2Sbrp7MFhkZyfTp09m8eTNxcXFUvrMKt99RhReeHsqoseO4qWw5tvwSyeBnnmfZzMkA7Inez3eLp7Mrai9NOj9AvdB6zJ85nzz58jBz2UzOnztP7/BuhNWvD8CWHTv4aeFCit9wA0169GbDzz9S5c5qDH3qMWZPX0DZMjfz6LCLNz9XrFiRdevWERQUxLfffstrr4/mnY+mALB18ybmf7uOnLly0aNNGK2ahFKjSmXXtt+sWc+e3XuYsXQGqsrAngP5+YefqVU3eXE6Y0zWZ4n/Kq1bt4727duTO3duABo3a8m5c2fZHPkzjz/84MUVz1+cvrFjmxYEBARwy803UeqmUuz6axfrV6/njx1/sHzRcgDOHD/J33v2kCM4mBp33EHJYsUAqFypMnuj/yEkJA+lb7yJm8uWA+D+++9n0qRJAJw4cYJevXqxc+dORITYM2ddx67XsBEFrneUkG7Xqinfb/glWeJfseZ71q9eT4emHQA4HXuaPbv3WOI3JhuyxJ+BEhKUvPnyMWfpaldbucCDrseJZZiTPleUZ8c8S4PGDQAoecyxbN2GDeTIkcO1bmBgIHHx8Wke/7nnnqNx48bMnTuXqKgo6jcMTXKw5OteEgqq0G9wP7r07HKZV2mMyeqsj/8qhYaGMm/ePM6cOcPJkydZ/e0ycuXKRanSN7FskWN6RlVly2+/u7aZs2g5CQkJ/B31D9F7oilbriwNGjVg+tTpXLhwAYCdu3cTezr1Sd5vKVeevdH/EBW1G4Avv/zStezEiROULFkScEwNmdQP69Zw/Pgxzp49w8JlK6hbq1qy5U0b1WPOl3Ncs4sdjDnIv4f/vcp3xxjjy7LNGX/kuJ6c2/+b22XXlbjdbfvJ339P0XYmpFi6jle9enW6dOlClSpVuOGGG6hcxZFIX3vnA14aOZwPJ7xN3IULdI9oxp23VwTgxhLFaNC6K/+djGXUa6O4Lud1dOreiX1799GxWUdUleL5C/JFGpOv5MyZkzdeHU/3Pl3IlSsXjcMauerxP/nkk/Tq1YvRo0fTunXrZNvdUbU6Q/r34WDMfnp2bJWsmweg2d31+f7vKO5r7aitlzskN69PfJ1CRQql6/0wxmQd2Sbxe8PIkSMZOXIkANv3XpwUfdL/zXA9TtrVE9awLu+9NgqA3YGBAAQEBPD4iMd5fMTjwMWunoa1a9Ow9sV5asa+PO7ifho1JaxRU4Bk5aLr1q3Ln3/+6Xrerf8QANrfex/t771YLDVpTEnH8Pfs25OefXum56UbY7Iw6+oxxhg/Y2f8meR/48d4OwRjjAHsjN8YY/yOJX5jjPEzlviNMcbPWOI3xhg/47GLuyIyGbgHOKSqlZ1tBYGvgDJAFNBZVY9lxPH+eemOjNiNS84+6zJsX1F79/Hjxs10bd/68itnsEnvvU2/QY9f0TZzp89l26/beG7scx6KyhjjTZ48458CtLyk7WlghaqWB1Y4n2d7e/bu46u5i90ui4uL88gxVZWEhAQmvTfeI/s3xmRdHjvjV9W1IlLmkuZ2QCPn46nAauApT8XgaZ999hnvvvsu58+fp8LtVejQpRujnnqC6QuWEZ8QT9c2LZjx4as898p4fv9rF7WbdeT+e9tRIH8+vly6gtOxp4mPj+fDzz9kUK9BnDhxAjkbx3OPPUbrJk0Y9eablCxWjH7duwMw7q1XCQkJYWD/R5n44bssWDSPeI2jffv2vPjii0RFRdGiRQvuuusuIiMjqXD7nZw7e5YOLRtxS4WKvP7uhyycM5NZU97n/PkL1Kp2J++OfZbAwECmfjWXV977H/ny5ePW229NVifIGJO9ZPY4/qKqGuN8fAAomsnHzzA7duzgq6++Yv369QQHB9O15wPs/vsvGjVrwbtvjOXs2TO0ad+J2yuW5+URQxj/4RTmTnPUwZ/21Ty2b93OvJXzKHB9AeLi4pjw6QTy5M1Dzl3HCOvalfCwMDq0asXTY8e6Ev+CRfOY/tksVq9dya7du1i6cAVFSuWnbdu2rF27ltKlS7Nz506mTp1KnTp12L73CMsWL3AVjft75598vXAeq+b9H8HBwQx+5mW+nLOIJqH1ePmNiXy1fDZ58uWhd8fe3Fb5Nm+9tcYYD/PaDVyqqiKiqS0XkX5AP4DSpUtnWlzptWLFCiIjI6lVy1G2+MTJUxQqVJiHHxtGlzbNue666xjx4ljgiNvt64XWo8D1BQBHt8zbr7zNxh83cp0GEHPwIIeOHKFKpUocPnqUmEOH2HfmMPnz56dkiVJ8PPkj1qxbSZNWoQQFB3Lq1Cl27txJ6dKluemmm6hTp47bY/64fi3bt/5K/fCuAJw5e44ihQvy8y9bCK1bi4KFHWWbW7VrRdTfURn6fhljfEdmJ/6DIlJcVWNEpDhwKLUVVXUSMAmgZs2aqX5AeIuq0qtXL8aOHQtcrNVz+OABzsTGEnfhAufOnYVUekxy5c7lerxo9iKO/nuUWctnUeZUMJWbNOHs+fMARLRowbxly9h34gzt2nRwHXvwwMfpeX+fZLV6oqKiCAkJSSto2nXqwnsj+yVrXrB0xZW+fGNMFpbZwzkXAL2cj3sB8zP5+BmmSZMmzJo1i0OHHJ9dx48fY3/0Xl54ZhiDhj3NPe078tbYlwDImyeEk7Gpl1o+efIkhQoXIjg4mLU//cQ/+/e7lnVs1YrZS5awaMkC2rZuB0Dju8P4YsbnxMaeAmDfvn2uOC4VHBTsKvl8V/1Qli9ZyKEjjnLLR4+dYE/0fmpVu5N1P27k2NFjXLhwgWULl13ju2OM8WWeHM75JY4LuYVFJBoYBbwKzBCRB4E9QOeMOl7p57dmalnmSpUqMXr0aJo3b05CQgLxKjRu3oqg4CDuiehIfHw83duHs+q7n2hwV3UCAwKo1bQDPTpHUCB/vmT7atOhDQ/3fJi2jdpS+7bKVLj5Ztey28qX51RsLMWKFadoUUdsjULD+HPnn4RHNCcoOJA8efLw2WefEeis+JlUp2496dDibm6rfCevv/shg4c9wz339SNBEwgOCmb8mJHcVaMKzw4dyH333Ee+fPmoWLliut4DY0zW5MlRPfelsqiJp46Z2bp06UKXLo4Zq5KWZQbHjFnTFyxzlUBOnHc3UcNuHV2Pry90PdMXTwculmVO6scFC1J8IPV7cAD9HhyQrKsHYNu2bcmeDx3xPENHPO963qptewa1r5fiGL26tCe0Wyd3L9MYk83YnbvGGONnLPEbY4yfscRvjDF+xhK/Mcb4GUv8xhjjZyzxG2OMn8k2c+7Wn1A/Q/c3N8J9Nc2k8uTJw6lTp9i/fz9D+vdn/EefXvFxNqzfwOQPJvPhZx9eTZjGGHPF7Iw/A5QoUeKqkr4xxniDJf4MEBUVRbumDQG4r11L/vrj4h3BzTr1JvLXbcSePk2/J56lQeuu3NW8EysuqY+TkJBAi7otOHL0qOt5lRYXnxtjTEaxxJ/BWraJYOkiRwmiwwcPcODgEWpUqcyr70yiUf27+G7xdJbNnMy4l8ZxOkn9noCAANp2bMtXCxcCsOqHH7jj1lspXLCgV16HMSb7ssSfwVre047lSxzJe+mi+bRv3QyAFWu/542Jn1C7WUead+rD+XPnidkXk2zbDvd1YPp8x4fG/82eTff27TM3eGOMX8g2F3d9RdFixSlw/fX8seM3li6az8evOmaXVIXpk96mwi1lAdjtLKj27+F/XdsWL1mcIoULs+bHH9m0dSufjBuX+S/AGJPt2Rm/B7RsE8HkD9/j5H//cUelWwFoenc93v/0C1QdUwts37rd7ba9OnWi75NPEtGihdtqm8YYc62yzRn/+kfXZ2pZ5rQ0D2/Dqy+MZMDgoa62EUMGMGzUa9Rs2oGEhARuKF3K7RDO8MaNGThiBPd36HDNcRhjjDvZJvF7w6lTjolQypQpw/xv17naCxe5gS27DzifOcoy58qVk4mvj3Ktk9jVU7t+bWrXr+1q3/rHH1SuWDFZTX5jjMlIlvh9yMcTPmbm5C/5n/XtG2M8yBK/D+n7aF9euL+vt8MwxmRzdnHXGGP8jCV+Y4zxM5b4jTHGz1jiN8YYP+OVi7siEgWcBOKBOFWtea37XBN697XuIpnbPp+fofvLbOHh4Tz/+gRO/neCgX26JxtumqhZp968+twwalSp7IUIjTHe4s0z/saqWjUjkr4vUFUSEhK8HYbLkiVLyJc/v7fDMMb4IOvquQZRUVHceuut9OzZk3ZNG/LhO2/S+Z5mtG9+N++9+RoAsadPE9HjYWo17UD1sAhmzv8agCY1mzDupXG0bdSWzi07s2f3HgD27NvHPb17U7ddO9r06cPe/fsBGPzEQEY8/xSt2zenVv2qLFzs+EYSExNDaGgoVatWpXLlyqxb5zizL1OmDMeOOuoAxcfH8eTgAbQJq8eQ/n04feZMitfyzZr1dG3dlQ7NOjDkoSHExsZ69s0zxniNtxK/AstFJFJE+rlbQUT6ichGEdl4+PDhTA4v/Xbu3MnAgQN5atTLHDwQw1cLlzN76Sq2b/2VjT99z/JV6yle7AZ+/nYOm1bOo3njBq5t8+bLy4LVC+j2QDfGPjcWgOGjR3NfRAQ/zJ9P53vu4clXXnGtf+jQQRbOXspnn05n9KsvAvDFF1/QokULNm/ezK+//krVqlVTxLj777/o2qMPC1d+T568eflo6vRky48cPcar70xi8ozJzPlmDrdXuZ0pH07J+DfLGOMTvJX4G6hqdaAV8IiIhF66gqpOUtWaqlqzSJEimR9hOt10003UqVOH79eu5vt1q+nYqjGdwsPY9fdO9uzeReWK5Vmx9gdGjnmL736KJH++vK5tW7dv7fq9OXIzABs2b6Zza0d717Zt+SEy0rV+yxbhBAQEcGuFihw+4vgwrFWrFp9++ikvvPACW7duJW/ei/tPVKxESarXuguAe9rfy/cbfkm2fEPkr/z+5990b9ud9k3aM3/GfPZH78+w98gY41u8cnFXVfc5fx8SkblAbWCtN2K5ViEhIYCjj7/vwMfofH+vZMvLBR7kx6UzWbpyLS+8PoHGDe5i5OMPAyCIaz0R4XKuy3Gd63Filc/Q0FDWrl3L4sWL6d27N0888QQ9e/ZMtt2l+770UKoQFlqXlz5667IxGGOyvkw/4xeREBHJm/gYaA5sy+w4Mlr9uxszZ8YXxMY6CrcdPBDDv0cOs//AIXLnykm3jm14YkBvNm/d4drm6wWO/v6v539N1RpVAbirWjVmLVkCwIxFi6hXo0aax92zZw9Fixalb9++PPTQQ2zatCnFOjH7otkc+TMAS+bPpl6t6smW165xJz/8/IvrOsPp2NPs/nv3VbwLxpiswBtn/EWBuc6z0CDgC1Vdeq07vXvtGq+WZa4f2phdO/+ke0Q4ALlDQnh1/Pv8tncnz4x+gwAJIDg4iHfHPufa5sTxE7Rr3I4cOXLwxodvADBu5EgGjhzJu5MnU7hgQd4fMybN465evZpx48YRHBxMnjx5mDZtWop1ypa7hS+nTea54Y9xc/kK9Os1JNnyIoUK8vHbYxg2YBjnz58H4LGnH6NsubJX9V4YY3xbpid+Vd0FVMns43pCmTJl2Lbt4peVHg/2p8eD/ZOtU65cbpo1qu92+wcHPsiw54YlaytdsiSLpkxJse67b72f7Pnu36MB6NWrF7169UqxflRUFNv3HuH6goVYtOqHZMtyBzpKRX8z6+JxGje4i5nLZrqN0xiTvdhwTmOM8TNWltlLVmxc4e0QjDF+ys74jTHGz1jiN8YYP2OJ3xhj/IwlfmOM8TPZ5uLue0MXprF01xXsaScAnYc0vKZ4Lmf61OnkzJWTiM4RHj2OMcZcKtsk/qwkLi6Orr26ejsMY4yfssR/DWJjY+ncuTPR0dGcPnueAYOfoHSZm3n95ec4HRtLgYIF+Xz8KIoXLUKzTr2pUqki3/+8ic7twtl7+gy5Q3LzwMAH2LFtBy88+QJnz5ylQokbmThmDNfnz094z56MfvJJqleuzL9H/6XFPY3Z+P0Wfv9jB48NG8SFC+cJCBRmz55N+fLlvf12GGOyCOvjvwZLly6lRIkS/Prrr8z/dh0NGjXhlVHP8PaHk5m5ZAUdOndj1GvvuNY/f+EC3389gyEDeifbz9OPPs3QZ4cyf9V8KlWowKsTJ6Z53Kmff0rfB/qzcuk6Nm7cSKlSpTzx8owx2ZSd8V+DO+64g6FDh/LUU09RuVYD8hUowM4/dvBQ904AJMQnULpoAdf6ndq2TLGPk/+d5OR/J6ldrzYA3SIi6DVkSJrHrVm9Fu9MeIuYmP30fKC7ne0bY66IJf5rUKFCBTZt2sSSJUt4542x3FWvIbdUqMgX8752rVPOWRcHICR3rivaf1BgoGs6x3PnzrraO0bcS42qNflm5XLCw8P56KOPCAsLu8ZXY4zxF9bVcw32799P7ty5uf/+++nTfxBbNkdy9N8jrhLIFy5cYPsff6W5j7z58pIvfz42/rgRgOkLFlC/Vi3AUbBt82+OiqMLFy9wbRO1J4qbbipD3wf6065dO7Zs2eKJl2eMyaayzRn/oDfbZHpZ5q1btzJ8+HACAgKIU+H5MeMIDApk7KgRnDx5kvi4OJ7oex+Vbr0lzf2MfXes6+Ju+RI3ukoxD37gAXo9/jhTZs4krGm4a/0Fi+Yya84MgoKDKHVjSUaMGJHumI0xJtskfm9o0aIFLVq0AGD73iOu9mmzLt5TUM5NCWSAQcMHuR7fVvk2vlryFQAlj11cp8LNN/PDfMek6mdCivHM8GcBGPzI4wx+5HEAbrixQMa8GGOM37CuHmOM8TOW+I0xxs9k6cSfOOG4yTyq6pid3RiTZWXZxJ8zZ07+/fdfS/6ZSFU5cf488TEx3g7FGHMNsuzF3VKlShEdHc3hw4ddbXHHD7hdN+iE+8+3swdSrn/huli36/57Knea8Rw4dspte7z857b9SID7mM6cdr//q4krw2NSJT4mhtOff5HqMY0xvi/LJv7g4GDKli2brO2flzq7Xbf081vdtq/pPyBF29Zaw9ys6Rgumpb7h09z2z437zi37QOuz+e2/ZWZ7v9JriYuT8dkjMmavNLVIyItReQPEflLRJ72RgzGGOOvMj3xi0ggMBFoBVQC7hORSpkdhzHG+CtvnPHXBv5S1V2qeh6YDrTzQhzGGOOXJLNHxYhIJ6Clqj7kfN4DuEtVB12yXj+gn/PprcAfGRRCYeDIZdfKXBZT+lhM6eeLcVlM6ZORMd2kqkUubfTZq3aqOgmYlNH7FZGNqlozo/d7LSym9LGY0s8X47KY0iczYvJGV88+4MYkz0s524wxxmQCbyT+n4HyIlJWRHIAXYEFl9nGGGNMBsn0rh5VjRORQcAyIBCYrKru6yl7RoZ3H2UAiyl9LKb088W4LKb08XhMmX5x1xhjjHdl2Vo9xhhjro4lfmOM8TN+kfhFZLKIHBKRbd6OJZGI3Cgiq0Rku4j8JiKPeTsmABHJKSIbRORXZ1wvejsmcNzxLSK/iMgib8eSSESiRGSriGwWkY3ejgdARAqIyCwR+V1EdohIXS/Hc6vz/Un8+U9EhngzJmdcjzv/f28TkS9FJKe3YwIQkcecMf3myffJL/r4RSQUOAVMU9XK3o4HQESKA8VVdZOI5AUigQhV3e7luAQIUdVTIhIMfAc8pqo/ejmuJ4CaQD5VvcebsSQSkSigpqr6zA1AIjIVWKeq/3OOmsutqse9HBbgKteyD8cNm3u8GEdJHP+vK6nqGRGZASxR1SneiskZV2UclQxqA+eBpcAAVf0ro4/lF2f8qroWOOrtOJJS1RhV3eR8fBLYAZT0blSgDon1nIOdP149OxCRUkBr4H/ejMPXiUh+IBT4BEBVz/tK0ndqAvztzaSfRBCQS0SCgNzAfi/HA3Ab8JOqnlbVOGAN0METB/KLxO/rRKQMUA34ycuhAK5ulc3AIeAbVfV2XOOBJ4EEL8dxKQWWi0iks8SIt5UFDgOfOrvF/iciId4OKomuwJfeDkJV9wFvAP8AMcAJVV3u3agA2AY0FJFCIpIbCCf5za4ZxhK/l4lIHmA2MERV3c+QkslUNV5Vq+K4q7q28yuoV4jIPcAhVY30VgxpaKCq1XFUmn3E2aXoTUFAdeADVa0GxAI+Ufbc2e3UFpjpA7Fcj6MwZFmgBBAiIvd7NypQ1R3Aa8ByHN08m4F4TxzLEr8XOfvQZwOfq+ocb8dzKWc3wSqgpRfDqA+0dfanTwfCROQzL8bj4jxzRFUPAXNx9M16UzQQneQb2iwcHwS+oBWwSVUPejsQoCmwW1UPq+oFYA5Qz8sxAaCqn6hqDVUNBY4Bf3riOJb4vcR5EfUTYIeqvuXteBKJSBERKeB8nAtoBvzurXhU9RlVLaWqZXB0FaxUVa+fnYlIiPOiPM7ulOY4vqp7jaoeAPaKyK3OpiaAVwcLJHEfPtDN4/QPUEdEcjv/DpvguMbmdSJyg/N3aRz9+x6Z59Rnq3NmJBH5EmgEFBaRaGCUqn7i3aioD/QAtjr70wFGqOoS74UEQHFgqnMERgAwQ1V9ZgilDykKzHXkDYKAL1R1qXdDAuBR4HNn18ouoI+X40n8YGwG9Pd2LACq+pOIzAI2AXHAL/hO6YbZIlIIuAA84qmL834xnNMYY8xF1tVjjDF+xhK/Mcb4GUv8xhjjZyzxG2OMn7HEb4wxfsYSv/FbItJWRK7qzlZnFcyBSZ6XcA4RNMbn2XBOY66Cs77SIl+p9mrMlbAzfuNTRKSniGxxzgfwfyJSRkRWOttWOO9oRESmiMi7IvK9iOwSkU7O9uIistZZ+32biDR0trcUkU3O/a5wtvUWkfecj4uIyGwR+dn5U9/Z/oI45nNY7TzOYGeorwLlnMcZ54xzm3ObnCLyqThq9f8iIo0vPZ7z+SIRaeQsijfFGe9WEXncuXy1iLwmjvkR/kzyWgKdx/zZ+b70T+21p7Zv49/84s5dkzWIyO3As0A9VT0iIgWBqcBUVZ0qIg8A7wIRzk2KAw2AisACHLVpugHLVHWM8+7j3CJSBPgYCFXV3c79Xuod4G1V/c754bIMR5lcnPtvDOQF/hCRD3AUP6vsLGaX+A0g0SM4KlzfISIVcVTwrJDGS68KlEz89pBYMsMpSFVri0g4MApHnZkHcVSUrCUi1wHrRWQ5jlv8k732y+zb+ClL/MaXhAEzEyc2UdWj4phBKrEm+f8BrydZf56qJgDbRaSos+1nYLI4CuDNU9XNItIIWKuquxP36+bYTYFKzhIMAPnEUTkVYLGqngPOicghHOUa0tIAmOA81u8isgdIK/HvAm4WkQnAYhzVGRMlFu+LBMo4HzcH7kz8lgPkB8qn8trT2rfxU9bVY7Kyc0keC7gm3QnFMdPTFBHpmc59BQB1VLWq86dkkglpkh4nnqs/YYoj+d9cTmfMx4AqwGpgAMknnEk8dtLjCvBokljLqupyd6/9Mvs2fsoSv/ElK4F7nUWqcHbJfI+jKidAd2BdWjsQkZuAg6r6MY4kVx34EQgVkbJJ9nup5TgKnCXup+plYj2Jo+vHnXXOWHF28ZQG/gCigKoiEiAiN+Is4ywihYEAVZ2No6vrcqWUlwEPO8/sEZEK4qgWmuK1X8W+jR+wrh7jM1T1NxEZA6wRkXgcVRMfxTGj1HAcs0tdrtpkI2C4iFzAMc9yT1U9LI4ZsuaISACOmcWaXbLdYGCiiGzB8XexFscZcmqx/isi650XdL8GJiZZ/D7wgYhsxXGW31tVz4nIemA3jlLJO3BUhwTHlJufOmMDeOYyr/F/OLp9Nomjb+owjuseKV77Vezb+AEbzmmMMX7GunqMMcbPWOI3xhg/Y4nfGGP8jCV+Y4zxM5b4jTHGz1jiN8YYP2OJ3xhj/Mz/A4vfU+lMEmVRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"conscientiousness\",y=\"Total\",hue=\"Personality\",data=dataV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"agreeableness\",y=\"Total\",hue=\"Personality\",data=dataV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"extraversion\",y=\"Total\",hue=\"Personality\",data=dataV)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"openness\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"openness\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"neuroticism\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"conscientiousness\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"agreeableness\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Personality\",y=\"extraversion\",data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CORRELATION VISUALIZATION "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [\"openness\",\"neuroticism\",\"conscientiousness\",\"agreeableness\",\"extraversion\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "corrPearson = data[features].corr(method=\"pearson\")\n",
    "corrSpearman = data[features].corr(method=\"spearman\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "sns.heatmap(corrPearson,annot=True,cmap='RdYlGn', vmin=-1, vmax=+1)\n",
    "\n",
    "plt.title(\"Pearson Correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,8))\n",
    "sns.heatmap(corrSpearman,annot=True,cmap='RdYlGn', vmin=-1, vmax=+1)\n",
    "\n",
    "plt.title(\"Pearson Correlation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NORMALITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "openness ----------\n",
      "ShapiroResult(statistic=0.9279025793075562, pvalue=3.3038738411761415e-11)\n",
      "neuroticism ----------\n",
      "ShapiroResult(statistic=0.9523547887802124, pvalue=1.3906847229350205e-08)\n",
      "conscientiousness ----------\n",
      "ShapiroResult(statistic=0.938087522983551, pvalue=3.393595404688199e-10)\n",
      "agreeableness ----------\n",
      "ShapiroResult(statistic=0.9465893507003784, pvalue=2.8825972719914716e-09)\n",
      "extraversion ----------\n",
      "ShapiroResult(statistic=0.9462653994560242, pvalue=2.647000396649446e-09)\n"
     ]
    }
   ],
   "source": [
    "for i in features:\n",
    "    print(i,\"----------\")\n",
    "    print(shapiro(data[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HOMOGENEITY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeveneResult(statistic=6.874371499889138, pvalue=1.7474835327816553e-05)\n"
     ]
    }
   ],
   "source": [
    "print(levene(data[\"openness\"],data[\"neuroticism\"],data[\"conscientiousness\"],\n",
    "             data[\"agreeableness\"],data[\"extraversion\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CONVERSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      159\n",
      "Female    156\n",
      "Name: gender, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "1    159\n",
      "0    156\n",
      "Name: gender, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "serious        153\n",
      "extraverted     77\n",
      "responsible     40\n",
      "lively          24\n",
      "dependable      21\n",
      "Name: Personality, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "4    153\n",
      "1     77\n",
      "3     40\n",
      "2     24\n",
      "0     21\n",
      "Name: Personality, dtype: int64\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "columns = [\"gender\",\"Personality\"]\n",
    "for i in columns:\n",
    "    print(data[i].value_counts())\n",
    "    print(\"----\"*30)\n",
    "    data[i] = encode.fit_transform(data[i])\n",
    "    print(data[i].value_counts())\n",
    "    print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Personality\n",
    "\n",
    "* Dependable - 0\n",
    "* Extraverted - 1\n",
    "* Lively - 2\n",
    "* Responsible - 3\n",
    "* Serious - 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender\n",
    "\n",
    "* Female - 0\n",
    "* Male - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 315 entries, 0 to 314\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype\n",
      "---  ------             --------------  -----\n",
      " 0   gender             315 non-null    int64\n",
      " 1   age                315 non-null    int64\n",
      " 2   openness           315 non-null    int64\n",
      " 3   neuroticism        315 non-null    int64\n",
      " 4   conscientiousness  315 non-null    int64\n",
      " 5   agreeableness      315 non-null    int64\n",
      " 6   extraversion       315 non-null    int64\n",
      " 7   Personality        315 non-null    int64\n",
      " 8   Total              315 non-null    int64\n",
      "dtypes: int64(9)\n",
      "memory usage: 22.3 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# X & Y FOR MODELS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop(\"Personality\",axis=1)\n",
    "y = data[\"Personality\"]\n",
    "xTrain,xTest,yTrain,yTest = train_test_split(x,y,test_size=0.2,random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OLS MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:            Personality   R-squared (uncentered):                   0.838\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.833\n",
      "Method:                 Least Squares   F-statistic:                              180.5\n",
      "Date:                Thu, 01 Apr 2021   Prob (F-statistic):                    6.69e-93\n",
      "Time:                        14:55:03   Log-Likelihood:                         -413.48\n",
      "No. Observations:                 252   AIC:                                      841.0\n",
      "Df Residuals:                     245   BIC:                                      865.7\n",
      "Df Model:                           7                                                  \n",
      "Covariance Type:            nonrobust                                                  \n",
      "=====================================================================================\n",
      "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "gender                0.8878      0.160      5.549      0.000       0.573       1.203\n",
      "age                   0.0693      0.020      3.391      0.001       0.029       0.110\n",
      "openness             -0.1132      0.045     -2.542      0.012      -0.201      -0.025\n",
      "neuroticism          -0.2461      0.040     -6.170      0.000      -0.325      -0.168\n",
      "conscientiousness     0.1624      0.040      4.026      0.000       0.083       0.242\n",
      "agreeableness        -0.0339      0.042     -0.814      0.416      -0.116       0.048\n",
      "extraversion          0.2572      0.047      5.455      0.000       0.164       0.350\n",
      "Total                 0.0263      0.014      1.865      0.063      -0.001       0.054\n",
      "==============================================================================\n",
      "Omnibus:                       23.823   Durbin-Watson:                   2.145\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.638\n",
      "Skew:                          -0.783   Prob(JB):                     9.97e-07\n",
      "Kurtosis:                       3.425   Cond. No.                     6.17e+15\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[3] The smallest eigenvalue is 7.37e-27. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    }
   ],
   "source": [
    "ols = sm.OLS(yTrain,xTrain).fit()\n",
    "predict = ols.predict(xTest)\n",
    "print(ols.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LINEAR MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13007837918280846\n",
      "1.145383677741711\n"
     ]
    }
   ],
   "source": [
    "lm = LinearRegression().fit(xTrain,yTrain)\n",
    "predict = lm.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(lm,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(lm,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCR MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1292679638730239\n",
      "1.1457300705690932\n"
     ]
    }
   ],
   "source": [
    "pca = PCA()\n",
    "xRTrain = pca.fit_transform(scale(xTrain))\n",
    "xRTest = pca.fit_transform(scale(xTest))\n",
    "lmP = LinearRegression().fit(xRTrain,yTrain)\n",
    "predict = lm.predict(xRTest)\n",
    "\n",
    "R2CV = cross_val_score(lmP,xRTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(lmP,xRTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLS MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17305496783795574\n",
      "1.1374880636687663\n"
     ]
    }
   ],
   "source": [
    "pls = PLSRegression().fit(xTrain,yTrain)\n",
    "predict = pls.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(pls,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(pls,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 --------------------------------------------------------------------------------\n",
      "0.0864855444332786\n",
      "1.1973889802115782\n",
      "2 --------------------------------------------------------------------------------\n",
      "0.17305496783795574\n",
      "1.1374880636687663\n",
      "3 --------------------------------------------------------------------------------\n",
      "0.13680630117770792\n",
      "1.1428121304281458\n",
      "4 --------------------------------------------------------------------------------\n",
      "0.12866449193328858\n",
      "1.1458826191546723\n",
      "5 --------------------------------------------------------------------------------\n",
      "0.12922850153102067\n",
      "1.1456998729860184\n",
      "6 --------------------------------------------------------------------------------\n",
      "0.12926950530867481\n",
      "1.1457289812470872\n",
      "7 --------------------------------------------------------------------------------\n",
      "0.1292679638730248\n",
      "1.1457300705690925\n",
      "8 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "9 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "10 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "11 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "12 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "13 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "14 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "15 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "16 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "17 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "18 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n",
      "19 --------------------------------------------------------------------------------\n",
      "-1843544.5803585448\n",
      "1954.7608852674323\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,20):\n",
    "    plstuned = PLSRegression(n_components=i).fit(xTrain,yTrain) # best is 2\n",
    "    print(i,\"----\"*20)\n",
    "    R2CVtuned = cross_val_score(plstuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "    print(R2CVtuned)\n",
    "    errortuned = -cross_val_score(plstuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "    print(np.sqrt(errortuned))\n",
    "    \n",
    "plstuned = PLSRegression(n_components=2).fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RIDGE MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13398054664225612\n",
      "1.1426689504034717\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge().fit(xTrain,yTrain)\n",
    "predict = ridge.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(ridge,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(ridge,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "alph = np.random.uniform(0.01,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21294441889444488\n",
      "0.05984518457368705\n"
     ]
    }
   ],
   "source": [
    "cv = RidgeCV(alphas=alph,scoring=\"r2\",cv=10,normalize=True).fit(xTrain,yTrain)\n",
    "print(cv.best_score_)\n",
    "print(cv.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15062420744339383\n",
      "1.1390871644461191\n"
     ]
    }
   ],
   "source": [
    "ridgetuned = Ridge(alpha=0.05984518457368705,normalize=True).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(ridgetuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(ridgetuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LASSO MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.25794341163838197\n",
      "1.3976296599748894\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso().fit(xTrain,yTrain)\n",
    "predict = lasso.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(lasso,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(lasso,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0018064007190031926\n"
     ]
    }
   ],
   "source": [
    "cv = LassoCV(alphas=None,max_iter=1000000,normalize=True,verbose=False).fit(xTrain,yTrain)\n",
    "print(cv.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.14167019788413118\n",
      "1.1453037496785712\n"
     ]
    }
   ],
   "source": [
    "lassotuned = Lasso(alpha=0.0018064007190031926,normalize=True).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(lassotuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(lassotuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ENET MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.045433949082237676\n",
      "1.2923700556937876\n"
     ]
    }
   ],
   "source": [
    "enet = ElasticNet(random_state=0).fit(xTrain,yTrain)\n",
    "predict = enet.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(enet,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(enet,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.026159013876672955\n"
     ]
    }
   ],
   "source": [
    "cv = ElasticNetCV(alphas=None,random_state=0).fit(xTrain,yTrain)\n",
    "print(cv.alpha_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13719808888736088\n",
      "1.1443014235843765\n"
     ]
    }
   ],
   "source": [
    "enettuned = ElasticNet(alpha=0.026159013876672955,random_state=0).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(enettuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(enettuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.1771945831922297\n",
      "1.3077425114258832\n"
     ]
    }
   ],
   "source": [
    "knnr = KNeighborsRegressor().fit(xTrain,yTrain)\n",
    "predict = knnr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(knnr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(knnr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = {\"n_neighbors\": np.arange(1,20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 19}\n",
      "0.10793235111136008\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(knnr,n,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.05822300065970345\n",
      "1.2987391529540906\n"
     ]
    }
   ],
   "source": [
    "knnrtuned = KNeighborsRegressor(n_neighbors=19).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(knnrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(knnrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANN R MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(xTrain,yTrain)\n",
    "xRTrain = scaler.transform(xTrain)\n",
    "xRTest = scaler.transform(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00012893765305277416\n",
      "1.1861605191930944\n"
     ]
    }
   ],
   "source": [
    "mlpr = MLPRegressor().fit(xRTrain,yTrain)\n",
    "predict = mlpr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(mlpr,xRTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(mlpr,xRTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"alpha\":[0.0001,0.001,0.01,0.1,0.2],\n",
    "         \"hidden_layer_sizes\": [(20,20),(100,200,150),(300,200,250)],\n",
    "         \"activation\": [\"relu\",\"logistic\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'activation': 'logistic', 'alpha': 0.01, 'hidden_layer_sizes': (100, 200, 150)}\n",
      "0.2523470408383023\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(mlpr,params,cv=10,verbose=False,n_jobs=-1).fit(xRTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.30267836487330363\n",
      "1.5304036007460289\n"
     ]
    }
   ],
   "source": [
    "mlprtuned = MLPRegressor(activation=\"logistic\",alpha=0.01,\n",
    "                         hidden_layer_sizes=(100,200,150)).fit(xRTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(mlprtuned,xRTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(mlprtuned,xRTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REGRESSION TREES(CART) MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.46823101369374853\n",
      "1.482436861258869\n"
     ]
    }
   ],
   "source": [
    "cartr = DecisionTreeRegressor(random_state=42).fit(xTrain,yTrain)\n",
    "predict = cartr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(cartr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(cartr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"min_samples_split\":range(2,100),\n",
    "         \"max_leaf_nodes\":range(2,10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_leaf_nodes': 7, 'min_samples_split': 25}\n",
      "0.16233986052285526\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(cartr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0767292561985684\n",
      "1.3331397962623743\n"
     ]
    }
   ],
   "source": [
    "cartrtuned = DecisionTreeRegressor(max_leaf_nodes=7,min_samples_split=25,random_state=42).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(cartrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(cartrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BAGGING MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.03193053097426647\n",
      "1.2468773164702462\n"
     ]
    }
   ],
   "source": [
    "baggr = BaggingRegressor(random_state=42,bootstrap_features=True).fit(xTrain,yTrain)\n",
    "predict = baggr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(baggr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(baggr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "e = {\"n_estimators\": range(2,30)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 29}\n",
      "0.20962731447363675\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(baggr,e,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16318126769690752\n",
      "1.159964336784772\n"
     ]
    }
   ],
   "source": [
    "baggrtuned = BaggingRegressor(random_state=42,bootstrap_features=True,\n",
    "                             n_estimators=29).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(baggrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(baggrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FORESTS (RF) R MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.26678804567712777\n",
      "1.0973128433543808\n"
     ]
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(random_state=42).fit(xTrain,yTrain)\n",
    "predict = rfr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(rfr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(rfr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"max_depth\":range(1,20),\n",
    "         \"max_features\":[3,5,10,15, 20],\n",
    "         \"n_estimators\": [200,300,500,1000,2000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 13, 'max_features': 3, 'n_estimators': 500}\n",
      "0.25225183026735043\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(rfr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1675542945241507\n",
      "1.1523589139962715\n"
     ]
    }
   ],
   "source": [
    "rfrtuned = RandomForestRegressor(random_state=42,max_depth=13,\n",
    "                                 max_features=3,n_estimators=500).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(rfrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(rfrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRADIENT BOOSTING MACHINES (GBM) MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09178827880187843\n",
      "1.1270889645020885\n"
     ]
    }
   ],
   "source": [
    "gbmr = GradientBoostingRegressor().fit(xTrain,yTrain)\n",
    "predict = gbmr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(gbmr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(gbmr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"learning_rate\": [0.001, 0.01, 0.1, 0.2],\n",
    "          \"max_depth\": [3, 5, 8, 10],\n",
    "          \"n_estimators\": [200, 300, 500, 1000, 2000],\n",
    "          \"subsample\": [1, 0.5, 0.75]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 300, 'subsample': 0.5}\n",
      "0.25352853518096796\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(gbmr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.20393531806727222\n",
      "1.1366160437386206\n"
     ]
    }
   ],
   "source": [
    "gbmrtuned = GradientBoostingRegressor(learning_rate=0.01,max_depth=3,\n",
    "                                      n_estimators=300,subsample=0.5).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(gbmrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(gbmrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EXTREME GRADIENT BOOSTING (XGBOOST) R MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07842681477984167\n",
      "1.1971549675445117\n"
     ]
    }
   ],
   "source": [
    "xgr = XGBRegressor().fit(xTrain,yTrain)\n",
    "predict = xgr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(xgr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(xgr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"colsample_bytree\": [0.1, 0.5, 0.6, 0.9, 1],\n",
    "          \"n_estimators\": [100, 200, 500, 1000],\n",
    "          \"max_depth\": [2, 3, 4, 5],\n",
    "          \"learning_rate\": [0.1, 0.01, 0.5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = GridSearchCV(xgr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.188015319391594\n",
      "1.391641298283493\n"
     ]
    }
   ],
   "source": [
    "xgrtuned = XGBRegressor(colsample_bytree=0.5,n_estimators=1000,\n",
    "                       max_depth=5,learning_rate=0.5).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(xgrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(xgrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIGHT GRADIENT BOOSTING (LIGHT GBM) R MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.050360411953372555\n",
      "1.2914966363812102\n"
     ]
    }
   ],
   "source": [
    "lgbmr = LGBMRegressor().fit(xTrain,yTrain)\n",
    "predict = lgbmr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(lgbmr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(lgbmr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    \"n_estimators\": [100, 200, 500, 1000],\n",
    "    \"max_depth\": [2, 3, 4, 5, 6],\n",
    "    \"learning_rate\": [0.1, 0.01, 0.5]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'learning_rate': 0.01, 'max_depth': 2, 'n_estimators': 1000}\n",
      "0.2602833332801274\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(lgbmr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.04710563744814207\n",
      "1.2904299536538673\n"
     ]
    }
   ],
   "source": [
    "lgbmrtuned = LGBMRegressor(learning_rate=0.01,max_depth=2,\n",
    "                           n_estimators=1000).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(lgbmrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(lgbmrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CATEGORY BOOSTING (CATBOOST) R MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15066602367050064\n",
      "1.136795109777319\n"
     ]
    }
   ],
   "source": [
    "catbr = CatBoostRegressor(verbose=False).fit(xTrain,yTrain)\n",
    "predict = catbr.predict(xTest)\n",
    "\n",
    "R2CV = cross_val_score(catbr,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CV)\n",
    "error = -cross_val_score(catbr,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"depth\": [2,3,4,5,6,7],\n",
    "         \"learning_rate\": [0.1,0.01,0.001,0.5]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'depth': 2, 'learning_rate': 0.01}\n",
      "0.24838341685627202\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(catbr,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10037553901474028\n",
      "1.1840005656892258\n"
     ]
    }
   ],
   "source": [
    "catbrtuned = CatBoostRegressor(verbose=False,depth=2,learning_rate=0.01).fit(xTrain,yTrain)\n",
    "\n",
    "R2CVtuned = cross_val_score(catbrtuned,xTest,yTest,cv=10,scoring=\"r2\").mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = -cross_val_score(catbrtuned,xTest,yTest,cv=10,scoring=\"neg_mean_squared_error\").mean()\n",
    "print(np.sqrt(errortuned))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [lm,lmP,plstuned,ridgetuned,lassotuned,enettuned,\n",
    "         knnrtuned,mlprtuned,cartrtuned,baggrtuned,rfrtuned,gbmrtuned,xgrtuned,lgbmrtuned,catbrtuned]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "LinearRegression: \n",
      "Accuracy: -2.852234655234849\n",
      "----------------------------\n",
      "LinearRegression: \n",
      "Accuracy: -2.852234655234849\n",
      "----------------------------\n",
      "PLSRegression: \n",
      "Accuracy: -386.8327031194578\n",
      "----------------------------\n",
      "Ridge: \n",
      "Accuracy: 0.3449929360305598\n",
      "----------------------------\n",
      "Lasso: \n",
      "Accuracy: 0.33487913676989967\n",
      "----------------------------\n",
      "ElasticNet: \n",
      "Accuracy: 0.35607791792740817\n",
      "----------------------------\n",
      "KNeighborsRegressor: \n",
      "Accuracy: 0.24592381175129208\n",
      "----------------------------\n",
      "MLPRegressor: \n",
      "Accuracy: -2.8193140218442285\n",
      "----------------------------\n",
      "DecisionTreeRegressor: \n",
      "Accuracy: 0.22846615323270714\n",
      "----------------------------\n",
      "BaggingRegressor: \n",
      "Accuracy: 0.2290050468263899\n",
      "----------------------------\n",
      "RandomForestRegressor: \n",
      "Accuracy: 0.3176673031004319\n",
      "----------------------------\n",
      "GradientBoostingRegressor: \n",
      "Accuracy: 0.3557820125844049\n",
      "----------------------------\n",
      "XGBRegressor: \n",
      "Accuracy: -0.045306133170623486\n",
      "----------------------------\n",
      "LGBMRegressor: \n",
      "Accuracy: 0.36762871356538873\n",
      "----------------------------\n",
      "CatBoostRegressor: \n",
      "Accuracy: 0.34729164179214156\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTest)\n",
    "    accuracy = r2_score(yTest, predict)\n",
    "    print(\"-\" * 28)\n",
    "    print(name + \": \")\n",
    "    print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR FOR TRAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "LinearRegression: \n",
      "Accuracy: -2.43618632763295\n",
      "----------------------------\n",
      "LinearRegression: \n",
      "Accuracy: -2.43618632763295\n",
      "----------------------------\n",
      "PLSRegression: \n",
      "Accuracy: -487.659329166737\n",
      "----------------------------\n",
      "Ridge: \n",
      "Accuracy: 0.29058859020199046\n",
      "----------------------------\n",
      "Lasso: \n",
      "Accuracy: 0.28864745623079036\n",
      "----------------------------\n",
      "ElasticNet: \n",
      "Accuracy: 0.2903353084365706\n",
      "----------------------------\n",
      "KNeighborsRegressor: \n",
      "Accuracy: 0.22561844107417262\n",
      "----------------------------\n",
      "MLPRegressor: \n",
      "Accuracy: -2.690956295037808\n",
      "----------------------------\n",
      "DecisionTreeRegressor: \n",
      "Accuracy: 0.34462029265936356\n",
      "----------------------------\n",
      "BaggingRegressor: \n",
      "Accuracy: 0.8572729497710165\n",
      "----------------------------\n",
      "RandomForestRegressor: \n",
      "Accuracy: 0.8987125325565061\n",
      "----------------------------\n",
      "GradientBoostingRegressor: \n",
      "Accuracy: 0.561500407083835\n",
      "----------------------------\n",
      "XGBRegressor: \n",
      "Accuracy: 0.999993605073317\n",
      "----------------------------\n",
      "LGBMRegressor: \n",
      "Accuracy: 0.4843924311110833\n",
      "----------------------------\n",
      "CatBoostRegressor: \n",
      "Accuracy: 0.461350020106603\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTrain)\n",
    "    accuracy = r2_score(yTrain, predict)\n",
    "    print(\"-\" * 28)\n",
    "    print(name + \": \")\n",
    "    print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = pd.DataFrame(columns=[\"MODELS\",\"ACC\"])\n",
    "for model in models:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTest)\n",
    "    accuracy = r2_score(yTest, predict)\n",
    "    result = pd.DataFrame([[name,accuracy*100]],columns=[\"MODELS\",\"ACC\"])\n",
    "    r = r.append(result)\n",
    "    \n",
    "sns.barplot(x=\"ACC\",y=\"MODELS\",data=r,color=\"r\")\n",
    "plt.xlabel(\"ACC\")\n",
    "plt.xlim(0,50)\n",
    "plt.title(\"MODEL ACCURACY COMPARISON\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR FOR TRAIN VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = pd.DataFrame(columns=[\"MODELS\",\"ACC\"])\n",
    "for model in models:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTrain)\n",
    "    accuracy = r2_score(yTrain, predict)\n",
    "    result = pd.DataFrame([[name,accuracy*100]],columns=[\"MODELS\",\"ACC\"])\n",
    "    r = r.append(result)\n",
    "    \n",
    "sns.barplot(x=\"ACC\",y=\"MODELS\",data=r,color=\"r\")\n",
    "plt.xlabel(\"ACC\")\n",
    "plt.xlim(0,100)\n",
    "plt.title(\"MODEL ACCURACY COMPARISON\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOGISTIC REGRESSION MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7301587301587301\n",
      "0.6357142857142857\n",
      "1.126872339638022\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "lj = LogisticRegression(solver=\"liblinear\").fit(xTrain,yTrain)\n",
    "predict = lj.predict(xTest)\n",
    "\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(lj,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))\n",
    "print(\"----\"*30)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAUSSIAN NAIVE BAYES MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6984126984126984\n",
      "0.5476190476190476\n",
      "1.2661653143150793\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "gnb = GaussianNB().fit(xTrain,yTrain)\n",
    "predict = gnb.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(gnb,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6825396825396826\n",
      "0.5857142857142856\n",
      "1.3743685418725535\n"
     ]
    }
   ],
   "source": [
    "knnc = KNeighborsClassifier().fit(xTrain,yTrain)\n",
    "predict = knnc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(knnc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"n_neighbors\": np.arange(1,50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 12}\n",
      "0.5718461538461538\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(knnc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6825396825396826\n",
      "0.5357142857142857\n",
      "1.3273676061682242\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "knnctuned = KNeighborsClassifier(n_neighbors=12).fit(xTrain,yTrain)\n",
    "predicttuned = knnctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(knnctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANN C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(xTrain, yTrain)\n",
    "xRTrain = scaler.transform(xTrain)\n",
    "xRTest = scaler.transform(xTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7936507936507936\n",
      "0.669047619047619\n",
      "1.2084359562332876\n"
     ]
    }
   ],
   "source": [
    "mlpc = MLPClassifier().fit(xRTrain,yTrain)\n",
    "predict = mlpc.predict(xRTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(mlpc,xRTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"alpha\": [0.01,0.02,0.005,0.001,0.0001],\n",
    "         \"hidden_layer_sizes\": [(3,5),(5,3),(10,10,10),(100,200,150),(100,100,100)],\n",
    "         \"activation\": [\"relu\",\"logistic\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'activation': 'relu', 'alpha': 0.001, 'hidden_layer_sizes': (100, 200, 150)}\n",
      "0.6518461538461537\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(mlpc,params,cv=10,verbose=False,n_jobs=-1).fit(xRTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n",
      "0.6523809523809523\n",
      "1.247219128924647\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "mlpctuned = MLPClassifier(activation=\"relu\",alpha=0.001,\n",
    "                          hidden_layer_sizes=(100,200,150)).fit(xRTrain,yTrain)\n",
    "predicttuned = mlpctuned.predict(xRTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(mlpctuned,xRTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REGRESSION TREES(CART) C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5238095238095238\n",
      "0.6047619047619048\n",
      "1.558387444947959\n"
     ]
    }
   ],
   "source": [
    "cartc = DecisionTreeClassifier(random_state=42).fit(xTrain,yTrain)\n",
    "predict = cartc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(cartc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"max_depth\":range(1,20),\n",
    "         \"min_samples_split\":range(2,50)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 3, 'min_samples_split': 2}\n",
      "0.607076923076923\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(cartc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6031746031746031\n",
      "0.6357142857142857\n",
      "1.4907119849998598\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "cartctuned = DecisionTreeClassifier(random_state=42,max_depth=3,\n",
    "                                    min_samples_split=2).fit(xTrain,yTrain)\n",
    "predicttuned = cartctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(cartctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RANDOM FOREST (RF) C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6984126984126984\n",
      "0.6833333333333333\n",
      "1.351072472296296\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(random_state=42).fit(xTrain,yTrain)\n",
    "predict = rfc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(rfc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"max_depth\": [2, 5, 8, 10],\n",
    "          \"max_features\": [2, 5, 8],\n",
    "          \"n_estimators\": [10, 500, 1000],\n",
    "          \"min_samples_split\": [2, 5, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 8, 'max_features': 8, 'min_samples_split': 5, 'n_estimators': 500}\n",
      "0.6946153846153845\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(rfc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7142857142857143\n",
      "0.6857142857142857\n",
      "1.2909944487358056\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "rfctuned = RandomForestClassifier(random_state=42,max_depth=8,max_features=8,\n",
    "                                  min_samples_split=5,n_estimators=500).fit(xTrain,yTrain)\n",
    "predicttuned = rfctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(rfctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GBM C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n",
      "0.49523809523809514\n",
      "1.259881576697424\n"
     ]
    }
   ],
   "source": [
    "gbmc = GradientBoostingClassifier().fit(xTrain,yTrain)\n",
    "predict = gbmc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(gbmc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"learning_rate\": [0.001,0.01,0.1,0.05],\n",
    "         \"max_depth\": [3,5,10],\n",
    "         \"n_estimators\": [100,300,500,1000],\n",
    "         \"min_samples_split\":[2,5,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = GridSearchCV(gbmc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6984126984126984\n",
      "0.5547619047619048\n",
      "1.3153549825650603\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "gbmctuned = GradientBoostingClassifier(learning_rate=0.01,max_depth=5,\n",
    "                                      n_estimators=500,min_samples_split=5).fit(xTrain,yTrain)\n",
    "\n",
    "predicttuned = gbmctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(gbmctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBOOST C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "0.7142857142857143\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:07] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:07] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:08] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:08] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:08] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:08] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:08] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:08] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:37:08] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { verbose } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:37:08] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "0.638095238095238\n",
      "1.3273676061682242\n"
     ]
    }
   ],
   "source": [
    "xgc = XGBClassifier(verbose=False).fit(xTrain,yTrain)\n",
    "predict = xgc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(xgc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"n_estimators\": [100, 500, 1000, 2000],\n",
    "          \"subsample\": [0.6, 0.8, 1.0],\n",
    "          \"max_depth\": [3, 4, 5, 6],\n",
    "          \"learning_rate\": [0.1, 0.01, 0.02, 0.05],\n",
    "          \"min_samples_split\": [2, 5, 10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = GridSearchCV(xgc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:44:17] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:17] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "0.7142857142857143\n",
      "[18:44:19] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:19] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:20] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:20] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:21] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:21] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:22] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:22] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:23] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:23] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:24] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:24] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:25] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:25] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:26] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:26] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:27] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:27] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:44:28] WARNING: ../src/learner.cc:541: \n",
      "Parameters: { min_samples_split } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[18:44:28] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "0.669047619047619\n",
      "1.2786401506759573\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "xgctuned = XGBClassifier(n_estimators=1000,subsample=0.8,\n",
    "                        max_depth=5,learning_rate=0.01,min_samples_split=5).fit(xTrain,yTrain)\n",
    "predicttuned = xgctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(xgctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIGHTGBM C MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6984126984126984\n",
      "0.5761904761904761\n",
      "1.391584685352615\n"
     ]
    }
   ],
   "source": [
    "lgbmc = LGBMClassifier().fit(xTrain,yTrain)\n",
    "predict = lgbmc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(lgbmc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"n_estimators\": [100, 500, 1000, 2000],\n",
    "          \"subsample\": [0.6, 0.8, 1.0],\n",
    "          \"max_depth\": [3, 4, 5, 6],\n",
    "          \"learning_rate\": [0.1, 0.01, 0.02, 0.05],\n",
    "          \"min_child_samples\": [5, 10, 20]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = GridSearchCV(lgbmc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "0.6825396825396826\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "[LightGBM] [Warning] Accuracy may be bad since you didn't explicitly set num_leaves OR 2^max_depth > num_leaves. (num_leaves=31).\n",
      "0.6357142857142857\n",
      "1.3093073414159542\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "lgbmctuned = LGBMClassifier(n_estimators=1000,subsample=0.8,max_depth=6,\n",
    "                           learning_rate=0.02,min_child_samples=10).fit(xTrain,yTrain)\n",
    "predicttuned = lgbmctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(lgbmctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CATBOOST CLASSIFIER MODELS & ERROR & TUNING & PREDICT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6666666666666666\n",
      "0.7285714285714285\n",
      "1.4364860122855578\n"
     ]
    }
   ],
   "source": [
    "catbc = CatBoostClassifier(verbose=False).fit(xTrain,yTrain)\n",
    "predict = catbc.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predict))\n",
    "R2CV = cross_val_score(catbc,xTest,yTest,cv=10).mean()\n",
    "print(R2CV)\n",
    "error = mean_squared_error(yTest,predict)\n",
    "print(np.sqrt(error))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"iterations\": [200, 500],\n",
    "          \"learning_rate\": [0.01, 0.05, 0.1],\n",
    "          \"depth\": [3, 5, 8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'depth': 5, 'iterations': 200, 'learning_rate': 0.05}\n",
      "0.6906153846153845\n"
     ]
    }
   ],
   "source": [
    "cv = GridSearchCV(catbc,params,cv=10,verbose=False,n_jobs=-1).fit(xTrain,yTrain)\n",
    "print(cv.best_params_)\n",
    "print(cv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7777777777777778\n",
      "0.7023809523809523\n",
      "1.1818736805705576\n",
      "------------------------------------------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "catbctuned = CatBoostClassifier(verbose=False,depth=5,iterations=200,\n",
    "                                learning_rate=0.05).fit(xTrain,yTrain)\n",
    "predicttuned = catbctuned.predict(xTest)\n",
    "\n",
    "print(accuracy_score(yTest,predicttuned))\n",
    "R2CVtuned = cross_val_score(catbctuned,xTest,yTest,cv=10).mean()\n",
    "print(R2CVtuned)\n",
    "errortuned = mean_squared_error(yTest,predicttuned)\n",
    "print(np.sqrt(errortuned))\n",
    "print(\"----\"*30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelsC = [lj,gnb,knnctuned,mlpctuned,cartctuned,rfctuned,gbmctuned,xgctuned,lgbmctuned,catbctuned]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------\n",
      "LogisticRegression: \n",
      "Accuracy: 0.7301587301587301\n",
      "----------------------------\n",
      "GaussianNB: \n",
      "Accuracy: 0.6984126984126984\n",
      "----------------------------\n",
      "KNeighborsClassifier: \n",
      "Accuracy: 0.6825396825396826\n",
      "----------------------------\n",
      "MLPClassifier: \n",
      "Accuracy: 0.5873015873015873\n",
      "----------------------------\n",
      "DecisionTreeClassifier: \n",
      "Accuracy: 0.6031746031746031\n",
      "----------------------------\n",
      "RandomForestClassifier: \n",
      "Accuracy: 0.7142857142857143\n",
      "----------------------------\n",
      "GradientBoostingClassifier: \n",
      "Accuracy: 0.6984126984126984\n",
      "----------------------------\n",
      "XGBClassifier: \n",
      "Accuracy: 0.7142857142857143\n",
      "----------------------------\n",
      "LGBMClassifier: \n",
      "Accuracy: 0.6825396825396826\n",
      "----------------------------\n",
      "CatBoostClassifier: \n",
      "Accuracy: 0.7777777777777778\n"
     ]
    }
   ],
   "source": [
    "for model in modelsC:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTest)\n",
    "    accuracy = accuracy_score(yTest, predict)\n",
    "    print(\"-\" * 28)\n",
    "    print(name + \": \")\n",
    "    print(f\"Accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMPARISON REGRESSOR VISUALIZATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = pd.DataFrame(columns=[\"MODELS\",\"ACC\"])\n",
    "\n",
    "for model in modelsC:\n",
    "    name = model.__class__.__name__\n",
    "    predict = model.predict(xTest)\n",
    "    accuracy = accuracy_score(yTest, predict)\n",
    "    result = pd.DataFrame([[name,accuracy*100]],columns=[\"MODELS\",\"ACC\"])\n",
    "    r = r.append(result)\n",
    "    \n",
    "sns.barplot(x=\"ACC\",y=\"MODELS\",data=r,color=\"r\")\n",
    "plt.xlabel(\"ACC\")\n",
    "plt.title(\"MODEL ACCURACY COMPARISON\")\n",
    "plt.show()"
   ]
  }
 ],
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