IRIS using RFR.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "nuclear-wagon",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import datasets\n",
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "objective-weekly",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "modified-reality",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dimensional-investigator",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(iris.data,columns=iris.feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moved-energy",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "educated-creativity",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "annoying-style",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['flower_name']=df.target.apply(lambda x:iris.target_names[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "overhead-twelve",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "quiet-ontario",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "celtic-religion",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['target','flower_name'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "expected-irish",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "decreased-relation",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_t,X_v,y_t,y_v=train_test_split(X,y,test_size=0.33,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dedicated-state",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=RandomForestRegressor(n_estimators=10,min_samples_split=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "attempted-installation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(min_samples_split=3, n_estimators=10)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_t,y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "charitable-click",
   "metadata": {},
   "outputs": [
    {
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       "0.9900579966813453"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_v,y_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "simple-andorra",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "designing-issue",
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   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "facial-protocol",
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "small-ability",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "driving-dancing",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "expired-springer",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "female-execution",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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