Python/ml/Ice_Cream_Revenue_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ICE CREAM REVENUE PREDICTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [Ice Cream Revenue](https://www.kaggle.com/vinicius150987/ice-cream-revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Temperature</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.566884</td>\n",
       "      <td>534.799028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26.005191</td>\n",
       "      <td>625.190122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.790554</td>\n",
       "      <td>660.632289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.595335</td>\n",
       "      <td>487.706960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11.503498</td>\n",
       "      <td>316.240194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>22.274899</td>\n",
       "      <td>524.746364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>32.893092</td>\n",
       "      <td>755.818399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>12.588157</td>\n",
       "      <td>306.090719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>22.362402</td>\n",
       "      <td>566.217304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>28.957736</td>\n",
       "      <td>655.660388</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature     Revenue\n",
       "0      24.566884  534.799028\n",
       "1      26.005191  625.190122\n",
       "2      27.790554  660.632289\n",
       "3      20.595335  487.706960\n",
       "4      11.503498  316.240194\n",
       "..           ...         ...\n",
       "495    22.274899  524.746364\n",
       "496    32.893092  755.818399\n",
       "497    12.588157  306.090719\n",
       "498    22.362402  566.217304\n",
       "499    28.957736  655.660388\n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'IceCreamData.csv', encoding='unicode_escape')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Temperature    False\n",
       "Revenue        False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "plt.scatter(df['Temperature'], df['Revenue'])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this clearly shows that there is a linear relationship between the two; hence we'll make a simple Linear Regression model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values\n",
    "X, y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)  # splitting in the ratio 80:20 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions and Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9837324255882576"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions are 98.37% accurate."
   ]
  }
 ],
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