Advertising Sales Channel Prediction - (Practice Project - 6).ipynb

{
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  {
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   "metadata": {},
   "source": [
    "#### Practice Project - 6"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2604f943",
   "metadata": {},
   "source": [
    "# Advertising Sales Channel Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e3a98f",
   "metadata": {},
   "source": [
    "#### Sales Channel Prediction Case Study  \n",
    "\n",
    "When a company enters a market, the distribution strategy and channel it uses are keys to its success in the market, as well as market know-how and customer knowledge and understanding. Because an effective distribution strategy under efficient supply-chain management opens doors for attaining competitive advantage and strong brand equity in the market, it is a component of the marketing mix that cannot be ignored . \n",
    "\n",
    "The distribution strategy and the channel design have to be right the first time. The case study of Sales channel includes the detailed study of TV, radio and newspaper channel. The predict the total sales generated from all the sales channel.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "9338b144",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import important libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "43fc754b",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
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      "text/plain": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/dsrscientist/DSData/master/Advertising.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14f61c8",
   "metadata": {},
   "source": [
    "##### about features \n",
    "* TV : advertising dollars spent on TV for a single product in a given market(in thousands of dollars)\n",
    "* Radio : advertising dollars spent on Radio\n",
    "* Newspaper : advertising dollars spent on newspaper\n",
    "\n",
    "##### label\n",
    "* Sales : sales of a single product in given market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3f186103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  200 non-null    int64  \n",
      " 1   TV          200 non-null    float64\n",
      " 2   radio       200 non-null    float64\n",
      " 3   newspaper   200 non-null    float64\n",
      " 4   sales       200 non-null    float64\n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 7.9 KB\n"
     ]
    }
   ],
   "source": [
    "# summary of dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "c205963f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 5)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking shape of columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "3ea47076",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "TV            0\n",
       "radio         0\n",
       "newspaper     0\n",
       "sales         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking nulls \n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "db262d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# checking duplicate value\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "f02daa81",
   "metadata": {},
   "outputs": [
    {
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       "      <th>TV</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, TV, radio, newspaper, sales]\n",
       "Index: []"
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   "source": [
    "# checking empty value\n",
    "df.loc[df['sales']==\" \"]"
   ]
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   "cell_type": "code",
   "execution_count": 137,
   "id": "28a4248d",
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       "      <th>TV</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, TV, radio, newspaper, sales]\n",
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   "source": [
    "df.loc[df['radio']==\" \"]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 138,
   "id": "c20dc8a1",
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, TV, radio, newspaper, sales]\n",
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   "source": [
    "df.loc[df['newspaper']==\" \"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "899294aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, TV, radio, newspaper, sales]\n",
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    "df.loc[df['TV']==\" \"]"
   ]
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   "id": "5a909aa6",
   "metadata": {},
   "source": [
    "* there is no Null, Duplicate and Empty value in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "fd72d8f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "      <th>newspaper</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.879185</td>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.750000</td>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.500000</td>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.250000</td>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0          TV       radio   newspaper       sales\n",
       "count  200.000000  200.000000  200.000000  200.000000  200.000000\n",
       "mean   100.500000  147.042500   23.264000   30.554000   14.022500\n",
       "std     57.879185   85.854236   14.846809   21.778621    5.217457\n",
       "min      1.000000    0.700000    0.000000    0.300000    1.600000\n",
       "25%     50.750000   74.375000    9.975000   12.750000   10.375000\n",
       "50%    100.500000  149.750000   22.900000   25.750000   12.900000\n",
       "75%    150.250000  218.825000   36.525000   45.100000   17.400000\n",
       "max    200.000000  296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking stats of data \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365260dd",
   "metadata": {},
   "source": [
    "* Data distribution looks good in TV and Radio as values in quantile range are nearly equally and mean and std are also relatable but in Newspaper columns max of quantile range goes much higher so there might be outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "d707f51b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
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     "execution_count": 141,
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     "data": {
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zVR2Yb3slYBRQC1dO8Z6qflnUMYuq8ahQwLo6wH9E5DVVHetp4KUi63la1JwlZ2aSv0M4L2zJsK5N3rY0eb6/Qwh6B4/Yd8eg44UaDxEJAT4G2gMJwBIRmaSq63IVewxYp6rdRCQS2Cgio1X1RGHHLTTxUNX+hQRSBZgJeJx4GGOMMcaLvNPUciWwRVW3AYjIWOAWXE+rP0mBCiIiQDiwF8gq6qCedC7NQ1X3ut/AYyFhscV9G+OhkzUdJ9K3+jmS4Hay1q5ieZs3z1sOHN4GQOXyVkPqLfsOu+4Th4c85udIglv5xz/2/ZueRedSEXkQeDDXqmGqOizXcnVgd67lBE4NODlpCK7HqiThainprVp0MMVOPETkRuDP4u5njDHGGO/Q7OL38XAnGcOKKFJQJUP+N+oIrMQ10rUe8LOIzFfVA4UdtKjOpWsKeIMquLKavxcRqDHGGGN8yTtNLQlAzVzLNXDlALndCwxUVQW2iMh24BJgcWEHLarGo2u+ZQX2qOphj0M2xhhjjPd5Zx6PJUADEakLJAJ9gDvyldkFtAXmi0g14GJgW1EHLapz6c6/FK4xxhhjfOMsmlrORFWzRORxYDqu4bRfqOpaEXnYvX0oMAD4yt1KIsDzqppR1HGL3cfDGGOMMecHVY0H4vOtG5rr9ySgQ3GOaYmHMcYYE+iCYeZSY4wxxgQISzyMMcYY4zNemjLdGyzxMMYYYwKd1XgYY4wxxme8MKrFWyzxMMYYYwKdd+bx8ApLPIwxxphAZzUexhhjjPEVtT4exhhjjPEZq/EwxhhjjM9YHw9jjDHG+IzVeBhjjDHGZ6yPhzHGGGN8xmo8jDHGGOMz1sfDGGOMMT4TQDUejsI2iMgQEbnWl8EYY4wxpvg0O7vYL38pNPEANgPvi8gOEXlbRJr4KCav6dghjrV/zGPDugX0e+6xAst8MOh1NqxbwPJlP9O0SSMAatSIZeaM71izeg6rVs7micfv92XYAeWlNwfRuksfut/5cIHbVZU3P/iEzrfdR4+/P8K6jVtyti1YtJSufR6g82338dnIcb4KOSC1bdeapct/ZsWq2Tz9zEMFlnn73VdYsWo2vy76icaNG+asX712Lr/9Hs/83yYzZ94EH0UceNq2a83i5TNYtmoWTxVyjQe++zLLVs1iwaIpXJHrGlesVIGvRg3h9+XTWbRsGi2vbOqrsAPKrzsy6P71r9w8YgFfLN1+2vYRy3bQ+5uF9P5mIb1G/Ubz//3M/mOZAIxasZOeo36j16jf+Pe01RzPcvo6fHOWCk08VPVDVb0GaAPsBb4UkfUi8oqIXOSzCM8Rh8PBRx++Qddud3J54xvo3bs7l17aIE+Zzp1upEH9ulxyWSseeeR5Ph7yFgBZWVk8168/l18Rx3WtuvHII/ectq9x6X5Te4YO+m+h2+cvXMKuhCTiv/2c1/r9kwHvDQHA6XTy3/c/5pP3BzBp9KfEz5zD1u07fRV2QHE4HLw/6DV63XofV7boSM+/dePiS+rnKdO+Qxz16tWhaeMbefKJ/zBo8Ot5tne9qS/XX9uNuNbdfRh54HA4HLw76DX+duv9XN2iEz3/1rWAa9yGevXq0LxxW5564iXeH9w/Z9vAd15m1s/zuKpZR66/uhsbcyXYxsWZrQycs4EhtzTlhzuvZdqmFLbuOZSnzN3N6/DtHdfw7R3X8MS1DWhe/QIqlQkj7dAxxqzaxeg+V/H9ndeSnQ3TN6X66UxKiGwt/stPiqrxAEBVd6rq26raFLgD6AGs93pk59iVLZuydesOtm/fRWZmJuPGTeTmbh3zlOnWrSMjR38PwO+Ll1OpciWio6NISUljxco/ADh06DAbNmymemy0z88hELRocjmVKlYodPsvCxZxc6e2iAiNG13KwYOHSM/Yy5r1m6hVI5aa1WMICwujc9s2zJ6/yIeRB47mLRqzbdtOduzYTWZmJj9+P4UuXdrlKdOlazvGjBkPwNIlK6lUqSLVqkX6I9yAdPIa78y5xj9xU75rfFPXdowt4BpXqBDOtde1ZOQIV61dZmYmB/Yf9Pk5lHR/pO6nZuVy1KhUjrAQBx0bRDNnW3qh5adtSqHTRafuu85s5XhWNlnZ2RzLchJZvrQvwi65ginxEJEwEekmIqOBqcAmoKfXIzvHYqtHszshKWc5ITGZ2HzJQ/XYaBJ2nyqTmJB8WoJRu3YNmjRuxO+LV3g34CCVmr6H6KiInOVqURGkpmeQlp5BdFRknvVp6Xv8EWKJFxtbjcSE5JzlxMQUYmKr5SkTE1ONxFyf96SklFOfd1UmTPyKufMncs+9fXwSc6CJyXeNkwq9xrnKJLnK1K5Tk4yMvXw89G3m/jqJD4e8SblyZX0We6BIO3ScauGnkoVq4aVJP3y8wLJHM538tjODtvVd/wZR4WX4e7M6dP5yPu0/m0d46VCuqV3VJ3GXWJpd/JefFNW5tL2IfAEkAA8C8UA9Ve2tqhN8FN85IyKnrVPVYpUpX74c474dzjP/epWDBw+dVtacWf5rDq7rXsBqCvjnMBT2OfWkjKtQh3a30brVLfS89T4eePBOrr2upVfiDGR/5X4RGhpC4yYN+eKzb2hz3c0cOXKEp54tuI+I8cy87ek0ialMpTJhABw4lsmcbWlMubsVM+5vzdFMJz9tSD7DUYJckNR4vAMsBC5V1W6qOlpVD3tyUBF5UESWisjScxLlOZCYkEzNGrE5yzWqx5CcnLdNMCExmRo1T5WpXiOGJHeZ0NBQvvt2OGPGjGfChKm+CToIRUdFkJKWkbOcmpZBVERVqkVFkJKWnmd9ZMR5/g2mEImJKVSvEZOzXL16NCn5PstJSSlUz/V5j42Nzvm8p6SkAZCRvocpk2fQvHljH0QdWJLyXePY6tGkJKflLZOUr0ysq0xSYgpJiSksW7oKgEkTpuXp3GtcosJLk3roVA1H6qHjhTaXTN+UQqeLT9U+/757L7EVy1KlXCnCQhzcWC+KVcn7vB1yiabZWuyXvxTZ1KKqw1V1b3EPqqrDVLWFqrY4+9DOrSVLV1K/fl3q1KlJWFgYt912C5OnzMhTZsqUGdzVtxcAV13ZjAP7D+TcpIcPe5/1G7Yw+MNhPo89mMS1uppJ02ahqqz6Yz3h4eWJjKhCo0suYldCEglJKWRmZjJ11lxuaHW1v8MtkZYvW029enWoXbsGYWFh3NqrK/Hxs/KUif9pJrff3gOAFi2bcODAQVJT0ylXrizh4eUBKFeuLDfeeD3r1m3y+TmUdK5rXJtaOde4C1PzXeOpP82iTwHXOC0tg8TEZOo3qAtA67hr2bjBOpfm17BaRXbtO0Li/qNkOrOZvjmFuAtP74d08HgmyxL/JO7CqJx10RXKsCZlP0cznagqi3fvpW6V8r4Mv+QJoBqPoiYQixSRZwrbqKqDvBCP1zidTp586iXif/qGEIeDr0Z8y7p1m3jwH3cBMGz4SOKnzqJTpxvZuP5Xjhw9ygMPuE7/umtbctedvVi9Zh1Ll7iSlZdfHsjUabP9dj4l1XOvDmTJitXs23eAtt3v5NH77yIrKwuA3j260PqalsxfuITOt91H2TJlGPDi0wCEhobw4tOP8NAzL+F0OunRtQP1L6ztz1MpsZxOJ/96tj8/TviKkBAHo0Z+z4b1m7nv/tsB+OLzMcyYPocOHeNYuXo2R44e47GHnwcgKiqCUWM+AVzX/Ptxk5k1c57fzqWkcjqd9Hu2Pz9M+JKQkBBGj/yODes3c6/7Gn/pvsbtO8axfPVsjh49mnONAfo9+zrDPh9EqVJh7Ni+m8ceeb6wtzpvhTocPB93MY9OXE52tnJLw1jqVQ3nuzW7Afjb5TUB+GVrOlfXqkrZsJCcfS+PrkS7+tW4Y+wiQkS4JLIiPRvW8Mt5lBgB9KwWKajNHUBEkoGhhe2oqv0L25bvOBoSFnvmguasODNdHQhPpG/1cyTBrVRkPQAqlr/Qz5EErwOHtwFQuXw9P0cSvPYddt0nDg8peB4jc26Uf/xjVNWnvdQOPtq52FUYFf5vql960hVV45HsaXJhjDHGGD8KoCnTi0o8bEyBMcYYEwAKa70oiYpKPNr6LApjjDHGnL1gqPE4m9EsxhhjjPGDYEg8jDHGGBMY/DkvR3FZ4mGMMcYEOks8jDHGGOMzgTONhyUexhhjTKCzphZjjDHG+E4AJR5FPqvFGGOMMeZcshoPY4wxJtBZHw9jjDHG+Ir18TDGGGOM71iNhzHGGGN8xWo8jDHGGOM7VuNhjDHGGF9RSzyMMcYY4zOWeBhjjDHGV6zGwxhjjDG+Y4mHMcYYY3zFajyMMcYY4zOBlHjYs1qMMcaYAKfZxX95QkQ6ichGEdkiIv8upEyciKwUkbUiMveMx1T17qQjIhI4s5oYY4wx54Cqii/fLzUurth/a6vNmVNkjCISAmwC2gMJwBLgdlVdl6tMZeA3oJOq7hKRKFVNK+q4VuNhjDHGBDgv1XhcCWxR1W2qegIYC9ySr8wdwI+qugvgTEkH+KiPR/2IZr54m/PSlozlAFQsf6GfIwluBw5vA+BE+lY/RxK8SkXWA+CtWn39HEnwemHXaAB2NG3r50iCW50Vs3z+nprtlQqW6sDuXMsJwFX5ylwEhInIHKAC8KGqfl3UQa1zqTHGGBPgzqZzqYg8CDyYa9UwVR2Wu0hBb5VvORRoDrQFygILRWSRqm4q7H0t8TDGGGPOQ+4kY1gRRRKAmrmWawBJBZTJUNXDwGERmQc0xtU3pEDWx8MYY4wJcKpS7JcHlgANRKSuiJQC+gCT8pWZCFwvIqEiUg5XU8z6og5qNR7GGGNMgPPGPB6qmiUijwPTgRDgC1VdKyIPu7cPVdX1IjINWI1r/tTPVPWPoo5riYcxxhgT4LzUuRRVjQfi860bmm/5XeBdT49piYcxxhgT4Lw8Jdc5ZYmHMcYYE+C8VePhDZZ4GGOMMQHOEg9jjDHG+Iw1tRhjjDHGZ6zGwxhjjDE+4+Nn0v0llngYY4wxAc4b83h4iyUexhhjTIDLthoPY4wxxviKNbUYY4wxxmcCqXOpRw+JE5EaIjJeRNJFJFVEfhCRGt4OzhhjjDFnplr8l794+nTaL3E9kS4GqA5Mdq8zxhhjjJ9pthT75S+eJh6Rqvqlqma5X18BkV6MyxhjjDEeylYp9stfPE08MkTkThEJcb/uBPZ4MzBjjDHGBB9PE4/7gNuAFCAZ6OVeZ4wxxhg/U5Viv/zFo1EtqroLuNnLsXjd9Tdew0tv/IuQkBDGjZrAsI++yrP9wvp1GPjRqzS84hIGvfl/fP5/I3O2vfXhK9zQ/nr2ZOylS+vePo48sLRt15q333mZkJAQvh7xLR8M+vS0Mm+/+wodOsRx5OhRHn2oH6tWrQVg9dq5HDp0GKfTiTPLSVzr7j6OvuR76c1BzPt1MVUuqMyEUUNP266qvDV4KPMXLqFMmdK88Z9nuezi+gAsWLSUgYOH4szOpme3Tjxw122+Dj9g1G1zBe1evQtHiINVY+ew6JPJebY3aN+M65/thWYr2U4ns/qPImHpJgAeWfABxw8fQ53ZZDudjOj2ij9OocQrc21LqvzrUQhxcGj8VA58NTbP9tLNGxM16HWykpIBODJ7AfuHjwKgQt+ehHfvDKpkbtlOxmvvwolMn59DSRE0z2oRkX6q+o6I/A847bRU9Z9ei+wcczgcvDbw39zzt0dJSUrlhxkjmT1tLls2bc8ps2/ffga8+C7tboo7bf8fx05m5OfjeHdIfx9GHXgcDgfvD3qN7jffTWJiCr/MG098/Cw2btiSU6Z9hzjq1atD08Y30qJlEwYNfp22N/TM2d71pr7s3fOnP8IPCN1vas8dPW/mxQHvFbh9/sIl7EpIIv7bz1m9dgMD3hvCmOGDcTqd/Pf9jxk++E2ioyLo/cCT3NDqKurVre3jMyj5xCF0GHA3Y/sO5GDKXu6Z9DqbZy5jz+aknDI7fl3L5p+XAxB5SU26f/wEw9v2y9k+ps8bHP3zkM9jDxgOB1Wef4K0R58nKzWdmFEfc3Tub2Ru35Wn2LGVa0h/8qU860Iiq1KxT3eSet2PHj9BxMCXKd/xBg5PnuHLMyhRAmkCsTM1tax3/1wKLCvgFTCuaNaQnTt2s3tnIpmZWfw0YQZtO8flKbM340/WrFxHVmbWafsvWbiC/X/u91G0gat5i8Zs27aTHTt2k5mZyY/fT6FLl3Z5ynTp2o4xY8YDsHTJSipVqki1atZX2VMtmlxOpYoVCt3+y4JF3NypLSJC40aXcvDgIdIz9rJm/SZq1YilZvUYwsLC6Ny2DbPnL/Jh5IEjpkk9/tyRyv7d6WRnOlk3eREN2jfPUybzyPGc38PKlUZP/25milCq0cVkJSSRlZgMWVkcnj6HsnHXeX6AkBCkdGkIcSBlS+NMP7+7HQZNU4uqTnb/HOGbcLwnOiaK5MTUnOWUpFQaN2/kx4iCU2xsNRITknOWExNTaNGycZ4yMTHVSEw49c0xKSmF2NhoUlPTQZUJE79CFb78YgxffZm36tWcWWr6HqKjInKWq0VFkJqeQVp6BtFRkXnWr1m70R8hlngVoi/gYPLenOWDyXuJbVrvtHIXdWxBm363US6iIt/de6oGSlF6j/o3qLJi9GxWjfnFJ3EHktDICLJS0nKWnWnplGp0yWnlSl9+GTFjP8WZvoc/P/iUzG07cabv4cDI76ge/w16/DjHFi7j2KKA+i58zgVTU8tkCmhiOUlVA6ffh5ye3Wkg/UsFCCnwOntSxlWoQ7vbSElJIyKyKhMmjWDTpq389usSr8QarAr6XItIgTemAv4pDAAFXJgCrt+m6UvZNH0pNa+8mNbP9mJs34EAjLr1dQ6l7aNc1Yr0GfU8e7cmsXuxJXl5FPThy3eNT2zYTGKXO9Cjxyhz3ZVEDupPUvd7cFQIp1zctSR2vZPsQ4eIfPsVyt/UlsPxs3wTewkUTE0t7wHvA9uBo8Bw9+sQ8EdhO4nIgyKyVESWnqtA/6qUpFRiqlfLWY6OrUZaSoYfIwpOiYkpVK8Rk7NcvXo0KcmpecokJaVQvUZsznJsbDTJ7jIp7m9AGel7mDJ5Bs2b560tMWcWHRVBStqpz3ZqWgZREVWpFhVBSlp6nvWREVX9EWKJdzBlLxViquQsV4ipwsHUwvsd7V68kcq1oyh7QTgAh9L2AXBkzwE2TV9GTJPTa0vOd1lp6YRGR+Ush0RFntZcooePoEePAXDs18VIaCiOyhUpc1UzshJTyN63H7KcHJm9gNJXNPRp/CVNIDW1FJl4qOpcVZ0LNFXV3qo62f26A2hVxH7DVLWFqrY41wGfrTUr1lGnbk1q1IolLCyULt07MGvaXH+HFXSWL1tNvXp1qF27BmFhYdzaqyvx+b6FxP80k9tv7wFAi5ZNOHDgIKmp6ZQrV5bw8PIAlCtXlhtvvJ516zb5/BwCXVyrq5k0bRaqyqo/1hMeXp7IiCo0uuQidiUkkZCUQmZmJlNnzeWGVlf7O9wSKXnVNqrUjaZSzUgcYSFc1u1qtrg7kp5UufapLzLVGtUhJCyUo38eIqxsaUqVLwNAWNnS1GndiPSNCT6NPxCcWLuR0JrVCY2NhtBQyneM4+jc3/KUcVS9IOf3Ug0vBnGQve8AWSlplLr8UqRMaQDKXNn0tE6p55tAmkDM04fERYrIhaq6DUBE6hJgM5c6nU76v/AOX4wbQogjhO/HTGTLxm3cfrdrNMWYET8QEVWV8T+PJLxCebKzlXseup3O1/2NQ4cO88Gnb3DldS24oEpl5q+K58N3PuX70RP9fFYlj9Pp5F/P9ufHCV8REuJg1Mjv2bB+M/fdfzsAX3w+hhnT59ChYxwrV8/myNFjPPbw8wBERUUwaswnAISGhvD9uMnMmjnPb+dSUj336kCWrFjNvn0HaNv9Th69/y6yslwdonv36ELra1oyf+ESOt92H2XLlGHAi08Drmv64tOP8NAzL+F0OunRtQP1L7QRLQVRZzYzXhlB76/7ISEOVo+bS8bmRJr0vRGAlaNnc3HnljTq2YrsTCdZx08w8bEhAJSLqEjPYU8BIKEhrJv4G9vnrvbXqZRczmz2vv0/oj4eCA4HhyZNI3PbTsJ7dgXg0A9TKN+uNeG9uoHTiR4/QcYL/wXgxB8bODJrHjGjP0GdTk5s3MLBH3/y59n4XSB1HBBP+jmISCdgGLDNvaoO8JCqTvdgX60f0eyvxGiKsCXD9S2sYvkL/RxJcDtw2PXRP5G+1c+RBK9Ska7miLdq9fVzJMHrhV2jAdjRtK2fIwludVbMQn3clvFbTM9i5x7XJv/gl2oPTycQmyYiDYCTXY43qOrxovYxxhhjjG/4s89GcXna1ALQALgYKAM0dvWS16+9E5YxxhhjPJXt7wCKwaPEQ0ReBeKAy4B4oDOwALDEwxhjjPEzLWgIeAnl6UPiegFtgRRVvRdoDJT2WlTGGGOM8Vi2Fv/lL542tRxT1WwRyRKRikAaYL0ZjTHGmBIgO4BqPM6YeIhrmsnVIlIZ1+Rhy3BNILbYu6EZY4wxxhOB1NRyxsRDVVVEmqjqPmCoiEwDKqqqDUw3xhhjTLF42tSySERaquoSVd3hzYCMMcYYUzxBN6oFuAF4SER2AodxPUFJVfUKr0VmjDHGGI8EVVOLW2evRmGMMcaYsxZ0NR6qutPbgRhjjDHm7ARd4mGMMcaYkisYm1qMMcYYU0JlB07eYYmHMcYYE+iCagIxY4wxxpRsfpwBvdgs8TDGGGMCnHUuNcYYY4zPZIs1tRhjjDHGR6ypxRhjjDE+Y00txhhjjPEZG05rjDHGGJ+x4bTGGGOM8ZlA6uMhqt4NV0QC6XoYY4wxf5mq+rQK4uvqdxb7b+3fE0edMUYR6QR8CIQAn6nqwELKtQQWAb1V9fuijukobqDGGGOMCX4iEgJ8jOsJ9ZcBt4vIZYWUexuY7slxfdLU0iLmel+8zXlpafJ8ACqXr+fnSILbvsNbAXirVl8/RxK8Xtg1GoAT6Vv9HEnwKhXpuk/0qNXNz5EEt/G7Jvv8Pb00quVKYIuqbgMQkbHALcC6fOWeAH4AWnpy0DPWeIhIiIiMKl6sxhhjjPEVPYuXB6oDu3MtJ7jX5RCR6kAPYKinsZ4x8VBVJxApIqU8PagxxhhjfCdbiv8SkQdFZGmu14P5DltQH5D8Octg4Hl3ruART5tadgC/isgk4HDOu6sO8vSNjDHGGOMdZ9PUoqrDgGFFFEkAauZargEk5SvTAhgrrinbI4CbRCRLVScUdlBPE48k98sBVPBwH2OMMcb4gJf6eCwBGohIXSAR6APckbuAqtY9+buIfAVMKSrpAA8TD1Xt7z5oeVU9fKbyxhhjjPEdbwzeVdUsEXkc12iVEOALVV0rIg+7t3vcryM3jxIPEbkG+BwIB2qJSGPgIVV99Gze1BhjjDHnjree1aKq8UB8vnUFJhyqeo8nx/R0Ho/BQEdgj/vgq4DWHu5rjDHGGC/KPouXv3g8j4eq7nZ3HjnJ4x6sxhhjjPGeQJoi3NPEY7eIXAuoe1jtP4H13gvLGGOMMZ4KpKfTetrU8jDwGK6JQxKBJu5lY4wxxvhZ0DW1qGoGYHNFG2OMMSWQPxOJ4vKoxkNELhSRySKSLiJpIjJRRC70dnDGGGOMOTMvTZnuFZ42tXwDjANigFjgO2CMt4IyxhhjjOfOZsp0f/E08RBVHamqWe7XKAKrE60xxhgTtIKujwfwi4j8GxiLK+HoDfwkIlUAVHWvl+IzxhhjzBkEUk2Ap4lHb/fPh/Ktvw/X+Vp/D2OMMcZPsgMo9fB0VEvdM5cyxhhjjCmaxzOXikgj4DKgzMl1qvq1N4IyxhhjjOcCaTitpw+JexWIw5V4xAOdgQWAJR7GGGOMnwVOQ4vno1p6AW2BFFW9F2gMlPZaVMYYY4zxWDCOajmqqtkikiUiFYE0ArBD6TVxV/LsgH/icDiYOOYnRgwZnWd77fq1eGXQv7nk8ov45O3PGDV0bM62ib9/y5FDR8nOdpKV5eTuzg/6OvyA0bZda9565yVCQkIYOWIcgwd9elqZge++TPsOcRw9epRHH3qe1avWAlCxUgU++vgtLr2sAarKE4+8wJLFK3x9CiVe3TZX0O7Vu3CEOFg1dg6LPpmcZ3uD9s24/tleaLaS7XQyq/8oEpZuAuCRBR9w/PAx1JlNttPJiG6v+OMUSryX3hzEvF8XU+WCykwYdfpTwFWVtwYPZf7CJZQpU5o3/vMsl11cH4AFi5YycPBQnNnZ9OzWiQfuus3X4QeEpm2acd+rD+AICWHm2BmM/+SHPNtbd29D94d7AnDsyFGG/ecTdqzfQVjpMP477i3CSoXhCA1hYfyvfPvB+T21VCA9q8XTxGOpiFQGhgPLgEPAYm8F5Q0Oh4N+bz7N432eITU5nRHxw5g3fQHbN+/MKXPgzwO8//JHtOnUqsBjPPy3J9m/d7+vQg5IDoeDdwe9Ro+b7yYpMYXZ835kavwsNm7YklOmfYc21KtXh+aN29KiZRPeH9yf9jf0AmDgOy8z6+d53HPn44SFhVG2XJnC3uq8JQ6hw4C7Gdt3IAdT9nLPpNfZPHMZezYn5ZTZ8etaNv+8HIDIS2rS/eMnGN62X872MX3e4Oifh3weeyDpflN77uh5My8OeK/A7fMXLmFXQhLx337O6rUbGPDeEMYMH4zT6eS/73/M8MFvEh0VQe8HnuSGVldRr25tH59ByeZwOPjHgIfo3/cV9qTs4Z1J77Nk5mISNu/OKZO6O5WXb3uBwwcO0zSuGQ+/9Rj/7v4cmcczefX2lzh25BghoSG88f1AVsxZzqYVG/14Rv4VSKNaPGpqUdVHVXWfqg4F2gN3u5tcAkbDppeye0ciibuSycrM4ueJs2jTMW+C8eeefaxbtYGsLKefogx8zVs0Ztu2nezcsZvMzEx+/P4nburSLk+Zm7q2Y+yY8QAsXbKSSpUqUq1aJBUqhHPtdS0ZOWIcAJmZmRzYf9Dn51DSxTSpx587Utm/O53sTCfrJi+iQfvmecpkHjme83tYudJoAN2USooWTS6nUsUKhW7/ZcEibu7UFhGhcaNLOXjwEOkZe1mzfhO1asRSs3oMYWFhdG7bhtnzF/kw8sBQv0kDknckk7o7lazMLBZMns+V7a/KU2bjsg0cPnAYgE3LN1I1JiJn27EjxwAICQ0hNCwU1fP7Mx5IU6YXZ1TLrUArXPEuAFZ7KyhviIyOIDUpLWc5NTmdRs0u83h/VRgy5n1UlfEjJzF+9OQz73QeiomtRmJCcs5yUmIKzVs2zlsmJl+ZpBRiYquRleUkI2MvHw99m0aXX8rKFX/wQr8BHDly1GfxB4IK0RdwMPnUnH0Hk/cS27TeaeUu6tiCNv1uo1xERb6799S3dkXpPerfoMqK0bNZNeYXn8QdbFLT9xAddeoPYbWoCFLTM0hLzyA6KjLP+jVrz99v4oWpGl2VPckZOct7kjNo0PTiQsu369OeFXOW5Sw7HA7enTKI6DoxTPs6ns0rN3k13pIukEa1ePqQuP8DHgbWAH8AD4nIx94M7FwTOb0BrDgZ8gO3PMpdHR/gyb7P0eueHjS9qvGZdzoPeXKdCysTGhpC4yYN+eKzb2hz3c0cOXKEp57NP2edgQIacwv4KG+avpThbfvx4z8+oPWzvXLWj7r1db7q8hLj7n6X5n9vR80rC7/Zm8IVdP8QEQq6rRTwkTcFfo4Lvic3uuZy2vZuz9dvjchZl52dzbM3PcU/rr6P+k0aUOuiWt4KNCBko8V++Yuno1raAB1V9UtV/RK4Cdfw2gKJyIMislRElp6DGM+JtOR0qsVG5SxXi4kkIyWjiD3yykjdA7iaY+ZMm0/Dppee8xiDQVJiCtVrxOQsx1aPJiU5LW+ZpHxlYl1lkhJTSEpMYdnSVQBMmjCNxo0b+ibwAHIwZS8VYqrkLFeIqcLB1D8LLb978UYq146i7AXhABxK2wfAkT0H2DR9GTFNTq8tMWcWHRVBStqpe0hqWgZREVWpFhVBSlp6nvWREVX9EWKJticlI0/TSdWYCPamnv70jdqX1OHRtx/nrQfe4NC+05tejxw4zNqFf9A0rplX4y3pAqmpxdPEYyOQO52sSRFNLao6TFVbqGqLvxLcubRu5QZq1a1BbM0YQsNCaX9LW+bN+NWjfcuULUO58mVzfr+6TUu2btjmzXAD1vJlq6lXrza1atcgLCyMW3t1YWr8rDxlpv40iz639wCgRcsmHDhwkNTUdNLSMkhMTKZ+A9dEua3jrs3TKdW4JK/aRpW60VSqGYkjLITLul3NFndH0pMq166W83u1RnUICQvl6J+HCCtbmlLlXR12w8qWpk7rRqRvTPBp/MEirtXVTJo2C1Vl1R/rCQ8vT2REFRpdchG7EpJISEohMzOTqbPmckOrq/0dbomzZdVmYurGElWzGqFhobTqdj1Lfv49T5mI2Aj6ffoCHz79AcnbT3WerlilIuUqlgegVOlSXNGqMQlbzu/PcTAOp60KrBeRkyNZWgILRWQSgKre7I3gziWn08k7/xnMR9+8R0iIg0lj49m2aQe33uUK/ceRk6gaWYURU4dRvkJ5NDubPg/0onfc36lcpRLvfP4GAKGhIUwbP5OFcwJqUI/POJ1O+j3bnx8mfElISAijR37HhvWbuff+2wH48vMxzJg+h/Yd41i+ejZHjx7lsYefz9m/37OvM+zzQZQqFcaO7bt57JHnC3ur85Y6s5nxygh6f90PCXGwetxcMjYn0qTvjQCsHD2bizu3pFHPVmRnOsk6foKJjw0BoFxERXoOewoACQ1h3cTf2D43oLpr+cxzrw5kyYrV7Nt3gLbd7+TR++8iKysLgN49utD6mpbMX7iEzrfdR9kyZRjw4tOA6x7x4tOP8NAzL+F0OunRtQP1L7QRLfllO7P57JVPeeXr13CEOJg1bia7N++mQ99OAMwYPY3bnuxDhQsq8OCAhwH3/aXbs1wQVYUnBj2Fw+HA4RB+nbKAZbNLTAW7XwTSqBbxpJ+DiLQparuqzi1iX20Rc/1ZhGY8sTR5PgCVy1t1uTftO7wVgLdq9fVzJMHrhV2ueXVOpG/1cyTBq1Sk6z7Ro1Y3P0cS3Mbvmoyq+rRnz9N1+hQ78/hgx1i/9D7yeB4PTk0idhFwCTBVVTO9F5oxxhhjPBF0o1qAeUAZEakOzALuBb7yVlDGGGOM8ZyexX/+4mniIap6BLgV+J+q9gBsuIExxhhTAgRj51IRkWuAvsD97nUh3gnJGGOMMcURSJ1LPa3xeBJ4ARivqmtF5ELApjs0xhhjTLF4WuORkHvIrKpuA/7pnZCMMcYYUxyBU9/heeLxlbtj6RJcHU3nq+oa74VljDHGGE8FUlOLR4mHqrYWkVK4Jg6LA34SkXBVrVL0nsYYY4zxtkAaTutR4iEirYDr3a/KwBRgvvfCMsYYY4yn/Dk8trg8bWqZi2sSsbeAeFU94b2QjDHGGFMcQVfjgetZLdcBrYF/ikg2sFBVX/ZaZMYYY4zxSNDVeKjqPhHZhuuptDWAa4EwbwZmjDHGGM8EXY2HiGwFNgILgKHAvdbcYowxxpQM2R488LWk8LSppYGqBlJCZYwxxpw3Aift8Hzm0voiMktE/gAQkStE5CUvxmWMMcYYD2WjxX75i6eJx3BcU6ZnAqjqaqCPt4IyxhhjjOcC6em0nja1lFPVxSKSe12WF+IxxhhjTDEFUl8ITxOPDBGph7sZSUR6Aclei8oYY4wxHgu6KdOBx4BhwCUikghsB/p6LSpjjDHGeCzo5vEAEoEvgV+AKsAB4G7gdS/FZYwxxhgPBWNTy0RgH7AcSPJaNMYYY4wpNg3CeTxqqGonr0ZijDHGmKAnnmRJIjIM+J+qrin2G4gEThpmjDHGnAOqKmcude7cUqtrsf/WTtw1xacxnuRpjUcr4B4R2Q4cBwRQVb3Ca5EZY4wxxiPB2Mej8195kwrl6v6V3U0RDh7ZDsDhIY/5OZLgVv7xjwHY0bStnyMJXnVWzAKgR61ufo4keI3fNRmAE+lb/RxJcCsVWc/n7+mtUS0i0gn4EAgBPlPVgfm29wWedy8eAh5R1VVFHdPTp9PuLH64xhhjjPEFb8zjISIhwMdAeyABWCIik1R1Xa5i24E2qvqniHTGNfXGVUUd19MaD2OMMcaUUF4a1XIlsEVVtwGIyFjgFiAn8VDV33KVXwTUONNBPX1WizHGGGNKqOyzeInIgyKyNNfrwXyHrQ7szrWc4F5XmPuBqWeK1Wo8jDHGmAB3Nn08VHUYrqaRwhQ06qXANxKRG3AlHq3O9L6WeBhjjDEBzkvPakkAauZarkEBk4iKyBXAZ0BnVd1zpoNa4mGMMcYEOC/18VgCNBCRurgendIHuCN3ARGpBfwI3KWqmzw5qCUexhhjTIDzRo2HqmaJyOPAdFzDab9Q1bUi8rB7+1DgFaAq8H8iApClqi2KOq4lHsYYY0yA89Y8HqoaD8TnWzc01+8PAA8U55iWeBhjjDEBLjsIHxJnjDHGmBIqcNIOSzyMMcaYgOelUS1eYYmHMcYYE+As8TDGGGOMz3hpOK1X2JTpxhhjjPEZq/EwxhhjAlxQN7WIiAMIV9UDXojHGGOMMcXkrXk8vMGjphYR+UZEKopIeVyPw90oIs95NzRjjDHGeEJVi/3yF0/7eFzmruHojmsGs1rAXd4KyhhjjDGey0aL/fIXT5tawkQkDFfiMURVM0UkcOp1jDHGmCAWjKNaPgV2AOWBeSJSG7A+HsYYY0wJEHQ1Hqr6EfBRrlU7ReQG74RkjDHGmOIIpM6lHiUeIlINeBOIVdXOInIZcA3wuTeDO9fatW/N2++8QkiIgxEjxvHB+0NPK/POu6/QoWMcR44e45GHnmPVyrUArFk3j0OHDuN0OsnKchJ3/S2+Dj9g/Lojg3fnbSRble4Nq3Nfi7p5to9YtoP4jckAOLOV7X8eZvY/4qhUJoxRK3Yyfm0iAtSPCKd/u4aUDg3xw1mUbGWubUmVfz0KIQ4OjZ/Kga/G5tleunljoga9TlaS6zofmb2A/cNHAVChb0/Cu3cGVTK3bCfjtXfhRKbPz6Gka9qmGfe9+gCOkBBmjp3B+E9+yLO9dfc2dH+4JwDHjhxl2H8+Ycf6HYSVDuO/494irFQYjtAQFsb/yrcfjPHHKZR4L705iHm/LqbKBZWZMOr0+7Gq8tbgocxfuIQyZUrzxn+e5bKL6wOwYNFSBg4eijM7m57dOvHAXbf5OvwSJRgfEvcV8CXwH/fyJuBbAijxcDgcvD+oP7d0+zuJiSnMmT+B+J9msnHDlpwyHTrGUa9+HZpccSMtWzbhg8EDuDHu1pztXTrfwd49f/oj/IDhzFYGztnAJz2aUS28DH2//Z02dSOpVzU8p8zdzetwd/M6AMzdls7olTupVCaMtEPHGLNqFz/ceS1lQkPoF7+a6ZtSufmyWD+dTQnlcFDl+SdIe/R5slLTiRn1MUfn/kbm9l15ih1buYb0J1/Ksy4ksioV+3Qnqdf96PETRAx8mfIdb+Dw5Bm+PIMSz+Fw8I8BD9G/7yvsSdnDO5PeZ8nMxSRs3p1TJnV3Ki/f9gKHDxymaVwzHn7rMf7d/Tkyj2fy6u0vcezIMUJCQ3jj+4GsmLOcTSs2+vGMSqbuN7Xnjp438+KA9wrcPn/hEnYlJBH/7eesXruBAe8NYczwwTidTv77/scMH/wm0VER9H7gSW5odRX16tb28RmUHIFU4+FpH48IVR0HZAOoahbg9FpUXtCiRWO2bdvJjh27yczM5Ifvp9Cla/s8ZW7q0o4x34wHYMmSlVSqVJFq0ZH+CDdg/ZG6n5qVy1GjUjnCQhx0bBDNnG3phZaftimFThdF5yw7s5XjWdlkZWdzLMtJZPnSvgg7oJRqdDFZCUlkJSZDVhaHp8+hbNx1nh8gJAQpXRpCHEjZ0jjT93gv2ABVv0kDknckk7o7lazMLBZMns+V7a/KU2bjsg0cPnAYgE3LN1I1JiJn27EjxwAICQ0hNCw0oDr++VKLJpdTqWKFQrf/smARN3dqi4jQuNGlHDx4iPSMvaxZv4laNWKpWT2GsLAwOrdtw+z5i3wYecmTrVrsl794mngcFpGquJ+8KyJXA/u9FpUXxMRGk5CQnLOclJhMbEy1PGVi85VJTEohNsb1R1FVmTBpBHMXTOSee/v4JugAlHboONXCTyUL1cJLk374eIFlj2Y6+W1nBm3ru/4dosLL8Pdmdej85XzafzaP8NKhXFO7qk/iDiShkRFkpaTlLDvT0gmJOv06lb78MmLGfkrU/94k7ELXN0Fn+h4OjPyO6vHfUGPGOPTgYY4tWuaz2ANF1eiq7EnOyFnek5xBlejCP4vt+rRnxZxT19HhcPB+/GC+XD6SVfNXsnnlJq/GG6xS0/cQHXUqoasWFUFqegZp6RlER0XmWZ92nifQehb/+YunicczwCSgnoj8CnwNPOG1qLxA5PR1+b+FSAGFTpbp0PZvtL7uZnr2uI9/PHQX117X0itxnk/mbU+nSUxlKpUJA+DAsUzmbEtjyt2tmHF/a45mOvlpQ/IZjnIeKvDDnHfxxIbNJHa5g+Q+D3Fg7AQiB/UHwFEhnHJx15LY9U4SOvZGypah/E1tfRB0oCnwhlFgyUbXXE7b3u35+q0ROeuys7N59qan+MfV91G/SQNqXVTLW4EGtYJqikSkwH+Kgv63OJ8EXY2Hqi4H2gDXAg8BDVV1dWHlReRBEVkqIkvPTZh/XVJiCjVqxOQsx1aPITnXt0aAxMTkPGWqx0aTnJIKQIq7bEb6HqZMmkHzFo19EHXgiQovTeqhUzUcqYeOF9pcMn1TCp0uPtXM8vvuvcRWLEuVcqUIC3FwY70oViXv83bIAScrLZ3Q6Kic5ZCoyNOaS/TwEfSoq7r/2K+LkdBQHJUrUuaqZmQlppC9bz9kOTkyewGlr2jo0/gDwZ6UjDxNJ1VjItibuve0crUvqcOjbz/OWw+8waF9B0/bfuTAYdYu/IOmcc28Gm+wio6KICXtVM1TaloGURFVqRYVQUpaep71kRHnd+1o0NR4iMitJ1/AzcDFwEVAN/e6AqnqMFVtoaotzm24Z2/ZstVcWK8OtWvXICwsjJ69uhL/08w8Zab+NIvb7+gBQMuWTThw4CCpKemUK1eW8PDyAJQrV5Yb27Zi/TqrOi1Iw2oV2bXvCIn7j5LpzGb65hTiLjy9n8zB45ksS/yTuAtP/QGNrlCGNSn7OZrpRFVZvHsvdauU92X4AeHE2o2E1qxOaGw0hIZSvmMcR+f+lqeMo+oFOb+XangxiIPsfQfISkmj1OWXImVcyWCZK5ue1inVwJZVm4mpG0tUzWqEhoXSqtv1LPn59zxlImIj6PfpC3z49Ackb0/KWV+xSkXKVXR9bkuVLsUVrRqTsCXBp/EHi7hWVzNp2ixUlVV/rCc8vDyREVVodMlF7EpIIiEphczMTKbOmssNra72d7h+FUg1Hmca1dKtiG0K/HgOY/Eqp9PJc8++xviJIwgJcTDy6+/YsH4z991/BwBffP4N06f/QoeOcaxa8wtHjh7j0Yf6ARAVFcHosa6hXqEhIXw3bhIzf57nt3MpyUIdDp6Pu5hHJy4nO1u5pWEs9aqG890a12iAv11eE4BftqZzda2qlA07NVT28uhKtKtfjTvGLiJEhEsiK9KzYQ2/nEeJ5sxm79v/I+rjgeBwcGjSNDK37SS8Z1cADv0whfLtWhPeqxs4nejxE2S88F8ATvyxgSOz5hEz+hPU6eTExi0c/PEnf55NiZTtzOazVz7lla9fwxHiYNa4mezevJsOfTsBMGP0NG57sg8VLqjAgwMeBlz3mH7dnuWCqCo8MegpHA4HDofw65QFLJtdYip/S5TnXh3IkhWr2bfvAG2738mj999FVlYWAL17dKH1NS2Zv3AJnW+7j7JlyjDgxacBCA0N4cWnH+GhZ17C6XTSo2sH6l94/o5ogcAa1SLe7m0tIlqhXN0zFzRn5eCR7QAcHvKYnyMJbuUf/xiAHU2tP4S31FkxC4AetYr6vmP+ivG7JgNwIn2rnyMJbqUi66GqPu11Urdq42L/Md++Z5VfesZ4Oo8HItIFaAiUOblOVV/3RlDGGGOMCU6ezlw6FCgH3AB8BvQCFnsxLmOMMcZ4yJ/PXikuT4fTXquqfwf+VNX+uKZLr+m9sIwxxhjjKVUt9stfPG1qOeb+eUREYoG9gHXcMMYYY0qAQKrx8DTxmCwilYF3geW4RrQM91ZQxhhjjPFcIE3L72nisQFwquoP7ifTNgMmeC0qY4wxxngskJ5O62kfj5dV9aCItALa43pa7Sdei8oYY4wxHguamUtzOfkk2i7AUFWdCJTyTkjGGGOMKY5g7FyaKCKfAu2At0WkNJ4nLcYYY4zxokDqXOpp8nAbMB3opKr7gCrAc94KyhhjjDGeC7oaD1U9Qq7nsqhqMmDPKzfGGGNKgEDqXOrxlOnGGGOMKZmCcTitMcYYY0qoQOrjYYmHMcYYE+CsxsMYY4wxPmN9PIwxxhjjM/6cEKy4LPEwxhhjApzVeBhjjDHGZwKpj4fNPmqMMcYYn7EaD2OMMSbAWR8PY4wxxvhMIDW1WOJhjDHGBLhASjzE28GKSOBcDWOMMeYcUFXx5fuFlqpe7L+1WScSfRrjSV5PPAKRiDyoqsP8HUcws2vsfXaNfcOus/fZNQ4uNqqlYA/6O4DzgF1j77Nr7Bt2nb3PrnEQscTDGGOMMT5jiYcxxhhjfMYSj4JZW6L32TX2PrvGvmHX2fvsGgcR61xqjDHGGJ+xGg9jjDHG+EyJTTxEpI6I/JFv3Wsi8i9/xeQJT2MUkRdEZIuIbBSRjr6IzR9EpKqIrHS/UkQkMddyx3xlnxKR//NXrMFKROJEZIr795tF5N/+jskYT4nIVyLSy99xmHPHZi71AxG5DOgDNARigZkicpGqOv0b2bmnqnuAJuBKyoBDqvqeiDyE6xpMz1W8D/Ccr2MMVCIiuJpLsz3dR1UnAZO8F5XxFhEJVdUsf8dhzF9VYms8zkRE5ojI2yKyWEQ2icj17vX3iMiPIjJNRDaLyDu59vlERJaKyFoR6Z9r/Q4ReVNEFrq3NxOR6SKyVUQezlXuORFZIiKr8+3/H3fNxUzgYg/CvwUYq6rHVXU7sAW48hxclkDyPdBVREqDq4YLVxK2wJ9BlXTumsD17pqh5cDnhXymO4nIBhFZANyaa/09IjLE/XttEZnl/jzPEpFaPj8hL8l1nYa7r80MESkrIvXc94ZlIjJfRC4RkRAR2SYulUUkW0Rau48zX0Tqi0ibXDV1K0SkgrsmaZ6IjBeRdSIyVEQc7v2KutecvG8tFpH67vWRIvKD+/6yRESuc69/TUSGicgM4Gs/XEqvEJHyIvKTiKwSkT9EpLeIvOI+9z/c53zarJoi0lxE5rr//aaLSIx7/T/d/warRWSs78/IFEfAJh5uoap6JfAU8Gqu9U2A3sDlQG8Rqele/x9VbQFcAbQRkSty7bNbVa8B5gNfAb2Aq4HXAUSkA9AAV4LQBGguIq1FpDmub+pNcd3gW548oIg8nDtxyaU6sDvXcoJ73XnDXROyGOjkXtUH+Fatt7MnLga+VtWmwLP5P9MiUgYYDnQDrgeiCznOEPdxrgBGAx95P3SfagB8rKoNgX1AT1yjI55Q1ebAv4D/c9c0bgIuA1oBy4Dr3UlxDVXd4i77mKo2wXVNj7rf40rgWVz3mnqcSvKKutcccN+3hgCD3es+BD5Q1ZbuOD/LVb45cIuq3vGXr0jJ0QlIUtXGqtoImAYMUdWW7uWyQNfcO4hIGPA/oJf73+8L4A335n8DTd2f5YLuuaYEKclNLYX9Acq9/kf3z2VAnVzrZ6nqfgARWQfUxvWH/jYReRDXecfgutGsdu9zsvp5DRCuqgeBgyJyTEQqAx3crxXucuG4bmwVgPGqesT9fjnV2Ko6tJBzKGh+/PPxD+4YXAnHRPfP+/wbTsDYqaqL3L8X9Jl2ANtVdTOAiIyi4Jkfr+HUH8qRwDsFlAlk21V1pfv3k/eIa4Hvcn2ZLu3+OR9oDdQF3gL+AcwFlri3/woMEpHRwI+qmuA+xmJV3QYgImNwJS7fU/S9Zkyunx+4f28HXJYrrooiUsH9+yRVPZnoBIs1wHsi8jYwRVXni0hPEekHlAOqAGuBybn2uRhoBPzsvk4hQLJ722pgtIhMACb45AzMWSvJicce4IJ866oA23MtH3f/dJL3XI7n+t0JhIpIXVzfWlqq6p8i8hVQpoB9svPtn+0+tgBvqeqnuQMSkacoftKQANTMtVwDSCrmMYLBBFw382ZAWVVd7ud4AsVhgDN8ps8mkQ225Df/faAasM9da5HffFzflGOBV3D1NYoD5gGo6kAR+Qm4CVgkIu3c++W/ZurBvUYL+N0BXJM/wXD/gT18phMNNKq6yV1bfBPwlrsp6TGgharuFld/sDL5dhNgrbtmOr8uuBLHm4GXRaSh9YcpuUpsU4uqHgKSRaQtgIhUwVU9d7Z9ACri+h94v4hUAzoXc//pwH0iEu6Op7qIROG6MfVwtx9XwFW9fSaTgD4iUtp9k2qAq9nhvOL+N56Dq8p0TNGlTQEK+0xvAOqKSD338u2F7P8brpomgL4Ef/+aA8B2EfkbuDrnikhj97bfcdWGZKvqMWAl8BCuhAQRqaeqa1T1bWApcIl7vytFpK67b0dvXNfwTPea3rl+LnT/PgN4/GQBEWlyTs64hBKRWOCIqo4C3gOauTdluO+xBY1i2QhEisg17mOEiUhD97Wvqaq/AP2AyrhqpE0JVZJrPAD+DnwsIu+7l/ur6tazOZCqrhKRFbiq77bhqjotzv4zRORSYKH7W8gh4E5VXS4i3+K6Ue3EfaMCVx8P975D8x1rrYiMA9YBWbjajoNuRIuHxuBqMutzpoImr8I+06p6zF3N/5OIZOD6Y9iogEP8E/hCRJ4D0oF7fRO5X/UFPhGRl4AwYCywSlWPi8hu4GQT1nxcCdsa9/JTInIDrpqTdcBUXE1VC4GBuPp4zMPV7Jp9hntNaRH5HdcXv5NJ4T9x3etW47ovzyO4+ypcDrwrItlAJvAI0B3X9d7BqSauHKp6QlzDaj8SkUq4rtNgXP1zRrnXCa6+Mvu8fwrmbNnMpcYYcxZEJA74l6p2PUPR3PvswNWckOGlsIwp8UpsU4sxxhhjgo/VeBhjjDHGZ6zGwxhjjDE+Y4mHMcYYY3zGEg9jjDHG+IwlHsYYY4zxGUs8jDHGGOMzlngYY4wxxmf+HwQsXJSzwXK2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(df.corr().abs(),annot=True,linecolor='black',linewidths=0.1,fmt='0.2f')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e5b19d8",
   "metadata": {},
   "source": [
    "* I see only TV and radio have better correlation with sales "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5df7a86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='newspaper', ylabel='sales'>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualing the relation of feature with label\n",
    "fig, axs = plt.subplots(1,3)\n",
    "df.plot(kind='scatter', x='TV',y='sales',ax=axs[0],figsize=(12,6))\n",
    "df.plot(kind='scatter', x='radio',y='sales',ax=axs[1])\n",
    "df.plot(kind='scatter', x='newspaper',y='sales',ax=axs[2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7932c06f",
   "metadata": {},
   "source": [
    "* I can see there is linera relationship b/W TV and sales \n",
    "* And b/w radio and sales it is also linear one\n",
    "* there is no linear relationship b/w newspaper and sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b3245707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0.000000\n",
       "TV           -0.069853\n",
       "radio         0.094175\n",
       "newspaper     0.894720\n",
       "sales         0.407571\n",
       "dtype: float64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking skewness \n",
    "df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b3d54c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualing data distribution\n",
    "plt.figure(figsize = (10,5))\n",
    "plotnumber = 1\n",
    "\n",
    "for column in df:\n",
    "    if plotnumber<=4:\n",
    "        ax = plt.subplot(2,2,plotnumber)\n",
    "        sns.distplot(df[column])\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "    \n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8971bd7",
   "metadata": {},
   "source": [
    "* Data looks skewed in newspaper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7e526fd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking outlier\n",
    "plt.figure(figsize=(10,5))\n",
    "graph = 1\n",
    "for column in df:\n",
    "    if graph<=4:\n",
    "        plt.subplot(1,4,graph)\n",
    "        ax = sns.boxplot(data = df[column])\n",
    "        plt.xlabel(column,fontsize=15)\n",
    "    graph+=1\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56067481",
   "metadata": {},
   "source": [
    "* outliers are only in newspaper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5594955d",
   "metadata": {},
   "source": [
    "####  Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1798b7f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing newspaper and unnamed as unnamed looks like index and of no use and newspaper is very less correlated with taget and\n",
    "# contains outliers\n",
    "df = df.drop(columns=['Unnamed: 0','newspaper'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "81c92877",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['TV','radio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "209013b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>radio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.948674</td>\n",
       "      <td>0.962247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.191314</td>\n",
       "      <td>1.040179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.647757</td>\n",
       "      <td>1.370710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.141020</td>\n",
       "      <td>1.142387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.452715</td>\n",
       "      <td>-0.748651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1.287908</td>\n",
       "      <td>-1.431436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-0.520949</td>\n",
       "      <td>-1.297017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.413091</td>\n",
       "      <td>-0.875492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.455843</td>\n",
       "      <td>1.177722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>0.968161</td>\n",
       "      <td>-0.937083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           TV     radio\n",
       "0    0.948674  0.962247\n",
       "1   -1.191314  1.040179\n",
       "2   -1.647757  1.370710\n",
       "3    0.141020  1.142387\n",
       "4    0.452715 -0.748651\n",
       "..        ...       ...\n",
       "195 -1.287908 -1.431436\n",
       "196 -0.520949 -1.297017\n",
       "197  0.413091 -0.875492\n",
       "198  1.455843  1.177722\n",
       "199  0.968161 -0.937083\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as over data starts from zero and no one will invest 0 dollars so i am gonna transfrom the data\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "scaler = PowerTransformer(method='yeo-johnson')\n",
    "df[features] = scaler.fit_transform(df[features].values)\n",
    "df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "9d58e764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "plotnumber = 1\n",
    "\n",
    "for column in df[features]:\n",
    "    if plotnumber<=4:\n",
    "        ax = plt.subplot(1,2,plotnumber)\n",
    "        sns.distplot(df[features][column])\n",
    "        plt.xlabel(column,fontsize=20)\n",
    "    \n",
    "    plotnumber+=1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a146fa13",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "b2eeffb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seperate feature and label\n",
    "x = df[features]\n",
    "y = df['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "682231ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.25, random_state = 348)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1148c7a7",
   "metadata": {},
   "source": [
    "## LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "46485f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "efcff2d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9106208912028522"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking train sccore \n",
    "lr.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "d9c9811d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8946891497980104"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking test score\n",
    "lr.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "5968cfa6",
   "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": [
    "# comparing actual and predicted value\n",
    "plt.scatter(y_test,y_pred)\n",
    "plt.xlabel('actual chance of admission')\n",
    "plt.xlabel('predict chance of adminssion')\n",
    "plt.title('actual vs predict model')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1d040959",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****accuracy****\n",
      "0.8946891497980104\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "y_pred = lr.predict(x_test)\n",
    "print(\"****accuracy****\")\n",
    "print(metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "d2ad25e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fbe4c98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,mean_absolute_error\n",
    "y_pred = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "cb58e6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4647375027304785"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MAE\n",
    "mean_squared_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "2b52c51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.3748669975328645"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MSE\n",
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e8c4678c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8613805367872736"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RMSE\n",
    "np.sqrt(mean_squared_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6faddaaa",
   "metadata": {},
   "source": [
    "* linear regression model biult successfully\n",
    "let's check if our model is overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "eb489562",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Regularisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "1a20fdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00031539363519797665"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge,Lasso,RidgeCV,LassoCV\n",
    "lassocv = LassoCV(alphas=None , max_iter=100 , normalize = True)\n",
    "lassocv.fit(x_train,y_train)\n",
    "alpha = lassocv.alpha_\n",
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "57964300",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=0.00031539363519797665)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=0.00031539363519797665)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=0.00031539363519797665)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now train with alpha\n",
    "lasso_reg = Lasso(alpha)\n",
    "lasso_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f209d61e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8946847773397697"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be22dfa",
   "metadata": {},
   "source": [
    "* lr score and lasso score is same so our model is not over fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "9d16d6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RidgeCV(alphas=array([0.001, 0.011, 0.021, 0.031, 0.041, 0.051, 0.061, 0.071, 0.081,\n",
       "       0.091]),\n",
       "        normalize=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RidgeCV</label><div class=\"sk-toggleable__content\"><pre>RidgeCV(alphas=array([0.001, 0.011, 0.021, 0.031, 0.041, 0.051, 0.061, 0.071, 0.081,\n",
       "       0.091]),\n",
       "        normalize=True)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RidgeCV(alphas=array([0.001, 0.011, 0.021, 0.031, 0.041, 0.051, 0.061, 0.071, 0.081,\n",
       "       0.091]),\n",
       "        normalize=True)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgecv = RidgeCV(alphas=np.arange(0.001,0.1,0.01),normalize=True)\n",
    "ridgecv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "456d34d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.001"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridgecv.alpha_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "9f32a988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.001)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.001)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.001)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model = Ridge(ridgecv.alpha_)\n",
    "ridge_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "922e2b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8946888652373238"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cbbed44",
   "metadata": {},
   "source": [
    "##  AdaBoost "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "babff940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostRegressor()"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "ada = AdaBoostRegressor()\n",
    "ada.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "d2a3a607",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model prediction on data\n",
    "y_pred = ada.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d7430557",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square Score :  0.9757470977206306\n"
     ]
    }
   ],
   "source": [
    "accuracy = metrics.r2_score(y_train,y_pred)\n",
    "print('R Square Score : ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "66c5f919",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R Square Score :-  0.9427578877491396\n"
     ]
    }
   ],
   "source": [
    "# predicting the test data with the result\n",
    "y_test_pred = ada.predict(x_test)\n",
    "# predicting the test data with the result\n",
    "accuracy = metrics.r2_score(y_test,y_test_pred)\n",
    "y_test_pred = ada.predict(x_test)\n",
    "print('R Square Score :- ',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa006f2f",
   "metadata": {},
   "source": [
    "* seems like model trained well now chcek weather model is overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "cabe24dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cross validation if the model is overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "291a7441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95230531710724"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "cross_val_score(ada,x,y,cv=10)\n",
    "cross_val_score(ada,x,y,cv=5).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e64544cc",
   "metadata": {},
   "source": [
    "model is not overfittng as cross_val_score is not more than ada model accuracy "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ecf94bd",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "38dbc899",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "params = {'n_estimators':[20,35,47,50,60,70,80,100], 'learning_rate':[0.25,0.30,0.40]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "78851d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "rnd_srch = RandomizedSearchCV(AdaBoostRegressor(),cv=5,param_distributions = params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "00fe7588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=AdaBoostRegressor(),\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.25, 0.3, 0.4],\n",
       "                                        &#x27;n_estimators&#x27;: [47, 50, 60, 70]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=AdaBoostRegressor(),\n",
       "                   param_distributions={&#x27;learning_rate&#x27;: [0.25, 0.3, 0.4],\n",
       "                                        &#x27;n_estimators&#x27;: [47, 50, 60, 70]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=AdaBoostRegressor(),\n",
       "                   param_distributions={'learning_rate': [0.25, 0.3, 0.4],\n",
       "                                        'n_estimators': [47, 50, 60, 70]})"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_srch.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "9a1b55f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostRegressor(learning_rate=0.4, n_estimators=70)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>AdaBoostRegressor(learning_rate=0.4, n_estimators=70)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "AdaBoostRegressor(learning_rate=0.4, n_estimators=70)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd_srch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "6b5dfcbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****accuracy post tuning****\n",
      "0.950171455728299\n"
     ]
    }
   ],
   "source": [
    "ada = AdaBoostRegressor(learning_rate=0.6,n_estimators=100)\n",
    "\n",
    "ada.fit(x_train,y_train)\n",
    "\n",
    "y_pred = ada.predict(x_test)\n",
    "print(\"****accuracy post tuning****\")\n",
    "print(metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "558c0b56",
   "metadata": {},
   "source": [
    "* as i see accuracy increased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03a38a77",
   "metadata": {},
   "source": [
    "## Knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "fd743ffc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "knn = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "131063c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "7c6ce85e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy =  0.963088258090401\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(x_test)\n",
    "print('accuracy = ',metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14959634",
   "metadata": {},
   "source": [
    "#### checking if model is overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "2b5e4117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.972953383014934"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "cross_val_score(knn,x,y,cv=10)\n",
    "cross_val_score(knn,x,y,cv=10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2333a53b",
   "metadata": {},
   "source": [
    "we can see model is not overfitting let's tune the parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e30a493",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "354e5ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "param_drid = {'algorithm': ['kd_tree','brute'],\n",
    "             'leaf_size':[3,4,5,6,7,8],\n",
    "             'n_neighbors':[3,4,7,9,11,13]\n",
    "             }\n",
    "gridsearch = GridSearchCV(estimator=knn, param_grid=param_drid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "deaa90be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=KNeighborsRegressor(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;leaf_size&#x27;: [3, 4, 5, 6, 7, 8],\n",
       "                         &#x27;n_neighbors&#x27;: [3, 4, 7, 9, 11, 13]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=KNeighborsRegressor(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;leaf_size&#x27;: [3, 4, 5, 6, 7, 8],\n",
       "                         &#x27;n_neighbors&#x27;: [3, 4, 7, 9, 11, 13]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsRegressor(),\n",
       "             param_grid={'algorithm': ['kd_tree', 'brute'],\n",
       "                         'leaf_size': [3, 4, 5, 6, 7, 8],\n",
       "                         'n_neighbors': [3, 4, 7, 9, 11, 13]})"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "6f6e35fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'kd_tree', 'leaf_size': 3, 'n_neighbors': 3}"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "f4275cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****accuracy post tuning****\n",
      "0.969051765800104\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsRegressor(algorithm = 'kd_tree', leaf_size = 10, n_neighbors=1)\n",
    "knn.fit(x_train,y_train)\n",
    "y_pred = knn.predict(x_test)\n",
    "print(\"****accuracy post tuning****\")\n",
    "print(metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35001f34",
   "metadata": {},
   "source": [
    "i see 0.6 % accuracy increased"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83025697",
   "metadata": {},
   "source": [
    "##  SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "87cb3b03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy :  0.9408405832676404\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "svr = SVR()\n",
    "\n",
    "# model training and score\n",
    "svr.fit(x_train,y_train)\n",
    "y_pred = svr.predict(x_test)\n",
    "print('accuracy : ',metrics.r2_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a854880",
   "metadata": {},
   "source": [
    "#### cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "70492e08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9671435043636812"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold,cross_val_score\n",
    "cross_val_score(svr,x,y,cv=10)\n",
    "cross_val_score(svr,x,y,cv=10).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4d6c58",
   "metadata": {},
   "source": [
    "model is not over fitting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c2bd35",
   "metadata": {},
   "source": [
    "### hyperparameter tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "7fb7a7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 20, 'gamma': 0.2}"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C':[1,5,10,20],\n",
    "              'gamma':[0.001,0.01,0.2,0.002]\n",
    "             }\n",
    "gridsearch = GridSearchCV(svr, param_grid)\n",
    "\n",
    "gridsearch.fit(x_train,y_train)\n",
    "\n",
    "# best param\n",
    "gridsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "20ad9fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "post tuning accuracy : 98.93007693930265\n"
     ]
    }
   ],
   "source": [
    "svr = SVR(C=100,gamma=0.5)\n",
    "\n",
    "# model training and score\n",
    "svr.fit(x_train,y_train)\n",
    "y_pred = svr.predict(x_test)\n",
    "\n",
    "print('post tuning accuracy :',metrics.r2_score(y_test,y_pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14896da0",
   "metadata": {},
   "source": [
    "* great accuracy is 99 %"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecff2fd3",
   "metadata": {},
   "source": [
    "#### As SVM model is giving best accuracy so i will save SVM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "12e4fde6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the model\n",
    "import pickle\n",
    "filename = \"Sales_Prediction\"\n",
    "pickle.dump(svr,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7e73f7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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