train_model.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import seaborn as sns\n",
    "import math\n",
    "import joblib\n",
    "\n",
    "from catboost import CatBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Timestep</th>\n",
       "      <th>Course</th>\n",
       "      <th>Faculty</th>\n",
       "      <th>Expected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>VX27870</td>\n",
       "      <td>Prof. Jadon Haney</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>SN11041</td>\n",
       "      <td>Prof. Kennedy Shields</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>CG17623</td>\n",
       "      <td>Prof. Brandon Silva</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>GW27856</td>\n",
       "      <td>Prof. Lorena Mays</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>SI10212</td>\n",
       "      <td>Prof. Haleigh Romero</td>\n",
       "      <td>329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89995</th>\n",
       "      <td>89995</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>BE26459</td>\n",
       "      <td>Prof. John Perkins</td>\n",
       "      <td>476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89996</th>\n",
       "      <td>89996</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>VZ14984</td>\n",
       "      <td>Prof. Kale Haynes</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89997</th>\n",
       "      <td>89997</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>XP22492</td>\n",
       "      <td>Prof. Tommy Hogan</td>\n",
       "      <td>295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89998</th>\n",
       "      <td>89998</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>YN11430</td>\n",
       "      <td>Prof. Christopher Dennis</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89999</th>\n",
       "      <td>89999</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>CK27485</td>\n",
       "      <td>Prof. Henry Knight</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id       Timestep   Course                   Faculty  Expected\n",
       "0          0  AY1810-AY1811  VX27870         Prof. Jadon Haney       386\n",
       "1          1  AY1810-AY1811  SN11041     Prof. Kennedy Shields       158\n",
       "2          2  AY1810-AY1811  CG17623       Prof. Brandon Silva       303\n",
       "3          3  AY1810-AY1811  GW27856         Prof. Lorena Mays       207\n",
       "4          4  AY1810-AY1811  SI10212      Prof. Haleigh Romero       329\n",
       "...      ...            ...      ...                       ...       ...\n",
       "89995  89995  AY1986-AY1987  BE26459        Prof. John Perkins       476\n",
       "89996  89996  AY1986-AY1987  VZ14984         Prof. Kale Haynes       222\n",
       "89997  89997  AY1986-AY1987  XP22492         Prof. Tommy Hogan       295\n",
       "89998  89998  AY1986-AY1987  YN11430  Prof. Christopher Dennis       187\n",
       "89999  89999  AY1986-AY1987  CK27485        Prof. Henry Knight      1103\n",
       "\n",
       "[90000 rows x 5 columns]"
      ]
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   "source": [
    "df"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90000 entries, 0 to 89999\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   Id        90000 non-null  int64 \n",
      " 1   Timestep  90000 non-null  object\n",
      " 2   Course    90000 non-null  object\n",
      " 3   Faculty   90000 non-null  object\n",
      " 4   Expected  90000 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 3.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "GK29879    157\n",
       "IX29155    151\n",
       "ZW13749    151\n",
       "LY11566    151\n",
       "AO12551    151\n",
       "          ... \n",
       "TV18763    124\n",
       "BW24288    123\n",
       "OL24651    123\n",
       "SA20222    121\n",
       "PA22070    121\n",
       "Name: Course, Length: 656, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Course'].value_counts()"
   ]
  },
  {
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   "execution_count": 7,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof. Leonard House         263\n",
       "Prof. Gage Davies           261\n",
       "Prof. Elyse Hood            261\n",
       "Prof. Ali Weaver            258\n",
       "Prof. Marcelo Rasmussen     258\n",
       "                           ... \n",
       "Prof. Cherish Barrera       124\n",
       "Prof. Sarah Kane            124\n",
       "Prof. Keaton Hines          123\n",
       "Prof. Dexter Bolton         123\n",
       "Prof. Houston Williamson    122\n",
       "Name: Faculty, Length: 560, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Faculty'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [],
   "source": [
    "df['Year'] = df['Timestep'].apply(lambda x: int(x[2:6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1275.369564009362"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "up_margine = df['Expected'].mean() + 3*df['Expected'].std()\n",
    "up_margine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df['Expected'], kde = True)\n",
    "plt.gcf().set_size_inches(8,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = df[df['Expected'] < up_margine]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 487
    },
    "execution": {
     "iopub.execute_input": "2022-03-25T17:33:19.593610Z",
     "iopub.status.busy": "2022-03-25T17:33:19.592864Z",
     "iopub.status.idle": "2022-03-25T17:33:19.945635Z",
     "shell.execute_reply": "2022-03-25T17:33:19.945080Z",
     "shell.execute_reply.started": "2022-03-25T17:33:19.593572Z"
    },
    "id": "MnXJo-hhR9_C",
    "outputId": "707c6878-8081-4800-96a5-aeeb40b4adbd"
   },
   "outputs": [
    {
     "data": {
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       "      <th>89997</th>\n",
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       "89999        0        0        0        0        0        0        0        0   \n",
       "\n",
       "       AM18690  AN11745  ...  ZS11345  ZS16168  ZS29879  ZU20965  ZU29326  \\\n",
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       "89999        0        0        0        0        0  \n",
       "\n",
       "[90000 rows x 656 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Course_dummies = pd.get_dummies(df['Course'])\n",
    "Course_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765
    },
    "execution": {
     "iopub.execute_input": "2022-03-25T17:33:19.947334Z",
     "iopub.status.busy": "2022-03-25T17:33:19.946725Z",
     "iopub.status.idle": "2022-03-25T17:33:20.132212Z",
     "shell.execute_reply": "2022-03-25T17:33:20.131338Z",
     "shell.execute_reply.started": "2022-03-25T17:33:19.947286Z"
    },
    "id": "j2V-LBOtR9_C",
    "outputId": "a0a71113-016c-44c0-c9b3-0db38bbc54e7"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Id</th>\n",
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       "      <th>Course</th>\n",
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       "<p>90000 rows × 661 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id       Timestep   Course                   Faculty  Expected  \\\n",
       "0          0  AY1810-AY1811  VX27870         Prof. Jadon Haney       386   \n",
       "1          1  AY1810-AY1811  SN11041     Prof. Kennedy Shields       158   \n",
       "2          2  AY1810-AY1811  CG17623       Prof. Brandon Silva       303   \n",
       "3          3  AY1810-AY1811  GW27856         Prof. Lorena Mays       207   \n",
       "4          4  AY1810-AY1811  SI10212      Prof. Haleigh Romero       329   \n",
       "...      ...            ...      ...                       ...       ...   \n",
       "89995  89995  AY1986-AY1987  BE26459        Prof. John Perkins       476   \n",
       "89996  89996  AY1986-AY1987  VZ14984         Prof. Kale Haynes       222   \n",
       "89997  89997  AY1986-AY1987  XP22492         Prof. Tommy Hogan       295   \n",
       "89998  89998  AY1986-AY1987  YN11430  Prof. Christopher Dennis       187   \n",
       "89999  89999  AY1986-AY1987  CK27485        Prof. Henry Knight      1103   \n",
       "\n",
       "       Year  AB18023  AB20928  AB25695  AD18559  ...  ZR13208  ZS11345  \\\n",
       "0      1810        0        0        0        0  ...        0        0   \n",
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       "...     ...      ...      ...      ...      ...  ...      ...      ...   \n",
       "89995  1986        0        0        0        0  ...        0        0   \n",
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       "89998  1986        0        0        0        0  ...        0        0   \n",
       "89999  1986        0        0        0        0  ...        0        0   \n",
       "\n",
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       "\n",
       "[90000 rows x 661 columns]"
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     "execution_count": 12,
     "metadata": {},
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   "source": [
    "df = pd.concat([df, Course_dummies.drop('ZZ23306', axis = 'columns')], axis='columns')\n",
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     },
     "execution_count": 13,
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    "Faculty_dummies = pd.get_dummies(df['Faculty'])\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>GW27856</td>\n",
       "      <td>Prof. Lorena Mays</td>\n",
       "      <td>207</td>\n",
       "      <td>1810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>AY1810-AY1811</td>\n",
       "      <td>SI10212</td>\n",
       "      <td>Prof. Haleigh Romero</td>\n",
       "      <td>329</td>\n",
       "      <td>1810</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89995</th>\n",
       "      <td>89995</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>BE26459</td>\n",
       "      <td>Prof. John Perkins</td>\n",
       "      <td>476</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89996</th>\n",
       "      <td>89996</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>VZ14984</td>\n",
       "      <td>Prof. Kale Haynes</td>\n",
       "      <td>222</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89997</th>\n",
       "      <td>89997</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>XP22492</td>\n",
       "      <td>Prof. Tommy Hogan</td>\n",
       "      <td>295</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89998</th>\n",
       "      <td>89998</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>YN11430</td>\n",
       "      <td>Prof. Christopher Dennis</td>\n",
       "      <td>187</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89999</th>\n",
       "      <td>89999</td>\n",
       "      <td>AY1986-AY1987</td>\n",
       "      <td>CK27485</td>\n",
       "      <td>Prof. Henry Knight</td>\n",
       "      <td>1103</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90000 rows × 1220 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id       Timestep   Course                   Faculty  Expected  \\\n",
       "0          0  AY1810-AY1811  VX27870         Prof. Jadon Haney       386   \n",
       "1          1  AY1810-AY1811  SN11041     Prof. Kennedy Shields       158   \n",
       "2          2  AY1810-AY1811  CG17623       Prof. Brandon Silva       303   \n",
       "3          3  AY1810-AY1811  GW27856         Prof. Lorena Mays       207   \n",
       "4          4  AY1810-AY1811  SI10212      Prof. Haleigh Romero       329   \n",
       "...      ...            ...      ...                       ...       ...   \n",
       "89995  89995  AY1986-AY1987  BE26459        Prof. John Perkins       476   \n",
       "89996  89996  AY1986-AY1987  VZ14984         Prof. Kale Haynes       222   \n",
       "89997  89997  AY1986-AY1987  XP22492         Prof. Tommy Hogan       295   \n",
       "89998  89998  AY1986-AY1987  YN11430  Prof. Christopher Dennis       187   \n",
       "89999  89999  AY1986-AY1987  CK27485        Prof. Henry Knight      1103   \n",
       "\n",
       "       Year  AB18023  AB20928  AB25695  AD18559  ...  Prof. Wade Taylor  \\\n",
       "0      1810        0        0        0        0  ...                  0   \n",
       "1      1810        0        0        0        0  ...                  0   \n",
       "2      1810        0        0        0        0  ...                  0   \n",
       "3      1810        0        0        0        0  ...                  0   \n",
       "4      1810        0        0        0        0  ...                  0   \n",
       "...     ...      ...      ...      ...      ...  ...                ...   \n",
       "89995  1986        0        0        0        0  ...                  0   \n",
       "89996  1986        0        0        0        0  ...                  0   \n",
       "89997  1986        0        0        0        0  ...                  0   \n",
       "89998  1986        0        0        0        0  ...                  0   \n",
       "89999  1986        0        0        0        0  ...                  0   \n",
       "\n",
       "       Prof. Warren Wall  Prof. Willow Moon  Prof. Wilson Hutchinson  \\\n",
       "0                      0                  0                        0   \n",
       "1                      0                  0                        0   \n",
       "2                      0                  0                        0   \n",
       "3                      0                  0                        0   \n",
       "4                      0                  0                        0   \n",
       "...                  ...                ...                      ...   \n",
       "89995                  0                  0                        0   \n",
       "89996                  0                  0                        0   \n",
       "89997                  0                  0                        0   \n",
       "89998                  0                  0                        0   \n",
       "89999                  0                  0                        0   \n",
       "\n",
       "       Prof. Yael Powell  Prof. Yosef Becker  Prof. Yurem Horton  \\\n",
       "0                      0                   0                   0   \n",
       "1                      0                   0                   0   \n",
       "2                      0                   0                   0   \n",
       "3                      0                   0                   0   \n",
       "4                      0                   0                   0   \n",
       "...                  ...                 ...                 ...   \n",
       "89995                  0                   0                   0   \n",
       "89996                  0                   0                   0   \n",
       "89997                  0                   0                   0   \n",
       "89998                  0                   0                   0   \n",
       "89999                  0                   0                   0   \n",
       "\n",
       "       Prof. Zain Cox  Prof. Zara Le  Prof. Zion Mcgee  \n",
       "0                   0              0                 0  \n",
       "1                   0              0                 0  \n",
       "2                   0              0                 0  \n",
       "3                   0              0                 0  \n",
       "4                   0              0                 0  \n",
       "...               ...            ...               ...  \n",
       "89995               0              0                 0  \n",
       "89996               0              0                 0  \n",
       "89997               0              0                 0  \n",
       "89998               0              0                 0  \n",
       "89999               0              0                 0  \n",
       "\n",
       "[90000 rows x 1220 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df, Faculty_dummies.drop('Prof. Zoie Mueller', axis = 'columns')], axis='columns')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "81jxVDmrR9_G",
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-2c70c166091f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Expected'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2485\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'center'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2486\u001b[0m         data=None, **kwargs):\n\u001b[0;32m-> 2487\u001b[0;31m     return gca().bar(\n\u001b[0m\u001b[1;32m   2488\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbottom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbottom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2489\u001b[0m         **({\"data\": data} if data is not None else {}), **kwargs)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1436\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1437\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1438\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1440\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2492\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0morientation\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'horizontal'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2493\u001b[0m                 \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msticky_edges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2494\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_patch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2495\u001b[0m             \u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_patch\u001b[0;34m(self, p)\u001b[0m\n\u001b[1;32m   2030\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2031\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2032\u001b[0;31m             \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2033\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_patch_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2034\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset_clip_path\u001b[0;34m(self, path, transform)\u001b[0m\n\u001b[1;32m    751\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 753\u001b[0;31m                 self.clipbox = TransformedBbox(Bbox.unit(),\n\u001b[0m\u001b[1;32m    754\u001b[0m                                                path.get_transform())\n\u001b[1;32m    755\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clippath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, bbox, transform, **kwargs)\u001b[0m\n\u001b[1;32m   1026\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bbox\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"'bbox' is not a bbox\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1028\u001b[0;31m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_isinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTransform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1029\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_dims\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dims\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1030\u001b[0m             raise ValueError(\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_isinstance\u001b[0;34m(_types, **kwargs)\u001b[0m\n\u001b[1;32m   2232\u001b[0m         \u001b[0mtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2233\u001b[0m     \u001b[0mnone_allowed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2234\u001b[0;31m     \u001b[0mtypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtp\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypes\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtp\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtype_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 12))\n",
    "plt.bar(df['Year'], df['Expected'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T17:35:25.019654Z",
     "iopub.status.busy": "2022-03-25T17:35:25.019374Z",
     "iopub.status.idle": "2022-03-25T17:35:25.073672Z",
     "shell.execute_reply": "2022-03-25T17:35:25.073053Z",
     "shell.execute_reply.started": "2022-03-25T17:35:25.019624Z"
    },
    "id": "hzXIuwkCR9_D"
   },
   "outputs": [],
   "source": [
    "min_year = 0\n",
    "max_year = 3000\n",
    "\n",
    "df['Year_'] = df['Year'].apply(lambda x: ( x - min_year ) / ( max_year - min_year ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_Expected = 0\n",
    "max_Expected = 3000\n",
    "\n",
    "df['Expected_'] = df['Expected'].apply(lambda x: ( x - min_Expected ) / ( max_Expected - min_Expected ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-03-25T17:35:29.881514Z",
     "iopub.status.busy": "2022-03-25T17:35:29.881071Z",
     "iopub.status.idle": "2022-03-25T17:35:29.889699Z",
     "shell.execute_reply": "2022-03-25T17:35:29.888806Z",
     "shell.execute_reply.started": "2022-03-25T17:35:29.881471Z"
    },
    "id": "_iAe2jdGR9_E",
    "outputId": "49ff07da-b1df-431b-83c3-52f33358d412"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                    int64\n",
       "Timestep             object\n",
       "Course               object\n",
       "Faculty              object\n",
       "Expected              int64\n",
       "                     ...   \n",
       "Prof. Zain Cox        uint8\n",
       "Prof. Zara Le         uint8\n",
       "Prof. Zion Mcgee      uint8\n",
       "Year_               float64\n",
       "Expected_           float64\n",
       "Length: 1222, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T17:35:34.613742Z",
     "iopub.status.busy": "2022-03-25T17:35:34.613112Z",
     "iopub.status.idle": "2022-03-25T17:35:34.923464Z",
     "shell.execute_reply": "2022-03-25T17:35:34.922694Z",
     "shell.execute_reply.started": "2022-03-25T17:35:34.613694Z"
    },
    "id": "eVgi5rMQR9_E"
   },
   "outputs": [],
   "source": [
    "x = df.drop(['Id', 'Timestep', 'Course', 'Faculty', 'Expected', 'Year_', 'Expected_'], axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T17:33:20.929378Z",
     "iopub.status.busy": "2022-03-25T17:33:20.929018Z",
     "iopub.status.idle": "2022-03-25T17:33:20.933690Z",
     "shell.execute_reply": "2022-03-25T17:33:20.932928Z",
     "shell.execute_reply.started": "2022-03-25T17:33:20.929348Z"
    },
    "id": "OTeJnSqkR9_E"
   },
   "outputs": [],
   "source": [
    "y = df['Expected']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T17:40:47.549898Z",
     "iopub.status.busy": "2022-03-25T17:40:47.549249Z",
     "iopub.status.idle": "2022-03-25T17:40:47.553913Z",
     "shell.execute_reply": "2022-03-25T17:40:47.553042Z",
     "shell.execute_reply.started": "2022-03-25T17:40:47.549855Z"
    },
    "id": "tCl_OgraR9_F"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "PTiscy86niON"
   },
   "outputs": [],
   "source": [
    "# x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
    "\n",
    "d = len(df)\n",
    "\n",
    "x_train = x[:int(d*0.8)]\n",
    "y_train = y[:int(d*0.8)]\n",
    "\n",
    "x_test = x[int(d*0.8):]\n",
    "y_test = y[int(d*0.8):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CatBoostRegressor(\n",
    "#                             iterations=1000,\n",
    "                            learning_rate=0.75,\n",
    "                            depth = 8,\n",
    "                            n_estimators = 2000\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6cd47a0025164cbdb0e445e62473fcf7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-78a7f622ab41>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x_train, y_train,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           plot = True)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/catboost/core.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, cat_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m   5349\u001b[0m             \u001b[0mCatBoostRegressor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_is_compatible_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss_function'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5351\u001b[0;31m         return self._fit(X, y, cat_features, None, None, None, sample_weight, None, None, None, None, baseline,\n\u001b[0m\u001b[1;32m   5352\u001b[0m                          \u001b[0muse_best_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogging_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn_description\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5353\u001b[0m                          \u001b[0mverbose_eval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_period\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mearly_stopping_rounds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/catboost/core.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X, y, cat_features, text_features, embedding_features, pairs, sample_weight, group_id, group_weight, subgroup_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval, metric_period, silent, early_stopping_rounds, save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr)\u001b[0m\n\u001b[1;32m   2087\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mlog_fixup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_cout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_cerr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2088\u001b[0m             \u001b[0mplot_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_get_train_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2089\u001b[0;31m             self._train(\n\u001b[0m\u001b[1;32m   2090\u001b[0m                 \u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2091\u001b[0m                 \u001b[0mtrain_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"eval_sets\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/catboost/core.py\u001b[0m in \u001b[0;36m_train\u001b[0;34m(self, train_pool, test_pool, params, allow_clear_pool, init_model)\u001b[0m\n\u001b[1;32m   1514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1515\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1516\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_clear_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minit_model\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1517\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_trained_model_attributes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._train\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "          verbose = False,\n",
    "          plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = pd.DataFrame(model.predict(x_test))\n",
    "\n",
    "y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'y_pred' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-0c62cdda8c7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
     ]
    }
   ],
   "source": [
    "for i in range(len(y_pred)):\n",
    "    y_pred[i] = y_pred[i] + 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.290419883372934"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Custom logger is already specified. Specify more than one logger at same time is not thread safe."
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b783688d94e540358e1956e80afe665e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x7fd615e99ee0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x, y, \n",
    "          verbose = False,\n",
    "          plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "aQpX9Q6_R9_G"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model_cat_34.joblib']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(model, \"model_late.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# normalizer = tf.keras.layers.Normalization(axis=-1)\n",
    "# normalizer.adapt(np.array(x_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def build_and_compile_model():\n",
    "#     model = keras.Sequential([\n",
    "#         norm,\n",
    "#         layers.Dense(32, activation='linear'),\n",
    "#         layers.Dense(32, activation='sigmoid'),\n",
    "#         layers.Dense(32, activation='linear'),\n",
    "#         layers.Dense(1, activation=\"linear\")\n",
    "#   ])\n",
    "\n",
    "#     model.compile(loss='mean_squared_error',\n",
    "#                 optimizer=tf.keras.optimizers.Adam(0.001))\n",
    "#     return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = build_and_compile_model()\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# %%time\n",
    "# history = model.fit(\n",
    "#     x_train,\n",
    "#     y_train,\n",
    "#     validation_split=0.2,\n",
    "#     verbose=True, \n",
    "#     epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidden_units1 = 256\n",
    "# hidden_units2 = 480\n",
    "# hidden_units3 = 160\n",
    "# learning_rate = 0.01\n",
    "\n",
    "# # Creating model using the Sequential in tensorflow\n",
    "# def build_model_using_sequential(norm):\n",
    "#     model = Sequential([\n",
    "#         norm,\n",
    "#         Dense(hidden_units1, kernel_initializer='normal', activation='relu'),\n",
    "#         Dropout(0.2),\n",
    "#         Dense(hidden_units2, kernel_initializer='normal', activation='relu'),\n",
    "#         Dropout(0.2),\n",
    "#         Dense(hidden_units3, kernel_initializer='normal', activation='relu'),\n",
    "#         Dense(1, kernel_initializer='normal', activation='linear')\n",
    "#     ])\n",
    "#     return model\n",
    "\n",
    "# # build the model\n",
    "# model = build_model_using_sequential(normalizer)\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# # loss function\n",
    "# mse = tf.keras.losses.MeanSquaredError()\n",
    "# model.compile(\n",
    "#     loss=mse, \n",
    "#     optimizer = keras.optimizers.Adam(learning_rate=learning_rate), \n",
    "#     metrics=[mse]\n",
    "# )\n",
    "# # train the model\n",
    "# history = model.fit(\n",
    "#     x_train.values, \n",
    "#     y_train.values, \n",
    "#     epochs=1, \n",
    "#     batch_size=1,\n",
    "#     validation_split=0.2\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# plt.plot(history.history['loss'], color = 'blue', label = 'loss')\n",
    "# plt.plot(history.history['val_loss'], color = 'orange', label = 'val_loss')\n",
    "\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sqrt(mean_squared_error(y_test, y_pred))*3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_list = x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Get numerical feature importances\n",
    "# importances = list(model.feature_importances_)\n",
    "# # List of tuples with variable and importance\n",
    "# feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "# # Sort the feature importances by most important first\n",
    "# feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "# # Print out the feature and importances \n",
    "# [print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# x.drop([pair[0] for pair in feature_importances if pair[1] == 0], axis = 'columns', inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# d = len(df)\n",
    "\n",
    "# x_train = x[:int(d*0.8)]\n",
    "# y_train = y[:int(d*0.8)]\n",
    "\n",
    "# x_test = x[int(d*0.8):]\n",
    "# y_test = y[int(d*0.8):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = CatBoostRegressor(\n",
    "# #                             iterations=1000,\n",
    "#                             learning_rate=0.75,\n",
    "#                             depth = 9,\n",
    "#                             n_estimators = 2000\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# model.fit(x_train, y_train,\n",
    "#           verbose = False,\n",
    "#           plot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred = pd.DataFrame(model.predict(x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.sqrt(mean_squared_error(y_test, y_pred))*3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joblib.dump(model, \"/content/drive/MyDrive/B.Tech/Hackrush'22 ML Challenge/model_Linear_3.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T18:09:38.397528Z",
     "iopub.status.busy": "2022-03-25T18:09:38.397136Z",
     "iopub.status.idle": "2022-03-25T18:09:38.401634Z",
     "shell.execute_reply": "2022-03-25T18:09:38.401029Z",
     "shell.execute_reply.started": "2022-03-25T18:09:38.397481Z"
    },
    "id": "-9sHhDtqR9_F"
   },
   "outputs": [],
   "source": [
    "# from sklearn.linear_model import LogisticRegression, LinearRegression, Lasso\n",
    "# from sklearn.svm import SVC\n",
    "# from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T18:09:58.605190Z",
     "iopub.status.busy": "2022-03-25T18:09:58.604904Z",
     "iopub.status.idle": "2022-03-25T18:09:58.609334Z",
     "shell.execute_reply": "2022-03-25T18:09:58.608660Z",
     "shell.execute_reply.started": "2022-03-25T18:09:58.605158Z"
    },
    "id": "AT0ho8usR9_F"
   },
   "outputs": [],
   "source": [
    "# model = LogisticRegression()\n",
    "# model = SVC(kernel='rbf', random_state = 1)\n",
    "# model = LinearRegression()\n",
    "# model = Lasso(alpha = 1, tol = 0.01)\n",
    "# model = RandomForestRegressor(n_estimators = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-25T18:10:00.271325Z",
     "iopub.status.busy": "2022-03-25T18:10:00.270534Z"
    },
    "id": "V-WpgKelR9_F"
   },
   "outputs": [],
   "source": [
    "# scores = cross_val_score(model, x[80000:], y[80000:], cv=5)\n",
    "# scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "_C3GJZIvGQuB"
   },
   "outputs": [],
   "source": [
    "# model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "nIMLp6fFpElu"
   },
   "outputs": [],
   "source": [
    "# y_pred = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "OXXYGFEAokLq"
   },
   "outputs": [],
   "source": [
    "# mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "A2Sa_-mXXV03"
   },
   "outputs": [],
   "source": [
    "# sns.displot(df['Expected'], kde = True)\n",
    "# plt.gcf().set_size_inches(8,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "vxfiK920Z67F"
   },
   "outputs": [],
   "source": [
    "# min_threshold, max_threshold = df['Expected'].quantile([0.01,0.99])\n",
    "# min_threshold, max_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "KdcA2DVKyK33"
   },
   "outputs": [],
   "source": [
    "# y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "XnMjWsWmySUe"
   },
   "outputs": [],
   "source": [
    "# df1 = df[(df[\"Expected\"] > min_threshold) & (df[\"Expected\"] < max_threshold)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "bGESlkymz4hP"
   },
   "outputs": [],
   "source": [
    "# sns.displot(df1['Expected'], kde = True)\n",
    "# plt.gcf().set_size_inches(8,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "EzzouQG9z7WA"
   },
   "outputs": [],
   "source": [
    "# x = df1.drop(['Id', 'Timestep', 'Course', 'Faculty', 'Expected', 'Year_'], axis = 'columns')\n",
    "# y = df1['Expected']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "8S2x0P500c2X"
   },
   "outputs": [],
   "source": [
    "# scores = cross_val_score(model, x, y, cv=5)\n",
    "# scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "r6U0UDFR0iDV"
   },
   "outputs": [],
   "source": [
    "# model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "8kjnGNzB1Opd"
   },
   "outputs": [],
   "source": [
    "# import joblib\n",
    "\n",
    "# joblib.dump(model, \"/content/drive/MyDrive/B.Tech/Hackrush'22 ML Challenge/model_Linear_3.joblib\")"
   ]
  }
 ],
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