salesprediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe20324f",
   "metadata": {},
   "source": [
    "# SALES PREDICTION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e04a3f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "15652a67",
   "metadata": {},
   "outputs": [
    {
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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",
       "    </tr>\n",
       "  </tbody>\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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading csv\n",
    "file = ('Advertising.csv')\n",
    "\n",
    "df = pd.read_csv(file)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "47de4a9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#exploring and preparing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7a03a57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(columns=[\"Unnamed: 0\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "972f5535",
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>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>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>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "63f7a1d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 4)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "57d59e44",
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "      <th>Newspaper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper\n",
       "0    230.1   37.8       69.2\n",
       "1     44.5   39.3       45.1\n",
       "2     17.2   45.9       69.3\n",
       "3    151.5   41.3       58.5\n",
       "4    180.8   10.8       58.4\n",
       "..     ...    ...        ...\n",
       "195   38.2    3.7       13.8\n",
       "196   94.2    4.9        8.1\n",
       "197  177.0    9.3        6.4\n",
       "198  283.6   42.0       66.2\n",
       "199  232.1    8.6        8.7\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,0:-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8ee7baaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      22.1\n",
       "1      10.4\n",
       "2       9.3\n",
       "3      18.5\n",
       "4      12.9\n",
       "       ... \n",
       "195     7.6\n",
       "196     9.7\n",
       "197    12.8\n",
       "198    25.5\n",
       "199    13.4\n",
       "Name: Sales, Length: 200, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df.iloc[:,-1]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e2585843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x22bf70e65e0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1050x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(df, x_vars=['TV', 'Radio', 'Newspaper'], y_vars='Sales', height=5, aspect=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "87582d00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "65740f09",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=43)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ef46aeb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "Sc = StandardScaler()\n",
    "x_train_scaled = Sc.fit_transform(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8bea8809",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test_scaled=Sc.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1cfe6ca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#using regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f599d127",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "88ea9945",
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0493734b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(x_train_scaled,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "39a4e7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=reg.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "390f9a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "623c7136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9260553930062493"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b0a09ec9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "84f8a7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22b8a1c8a90>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test,y_pred,c='g')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}