Task 1 - Sparks Foundation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Spark Foundation\n",
    "### Internship: Task 1\n",
    "#### Name : B.R.Vishnu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"http://bit.ly/w-data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 2 columns):\n",
      "Hours     25 non-null float64\n",
      "Scores    25 non-null int64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hours     Scores\n",
       "count  25.000000  25.000000\n",
       "mean    5.012000  51.480000\n",
       "std     2.525094  25.286887\n",
       "min     1.100000  17.000000\n",
       "25%     2.700000  30.000000\n",
       "50%     4.800000  47.000000\n",
       "75%     7.400000  75.000000\n",
       "max     9.200000  95.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### regression plot to see the relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b35fb7efc8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.regplot(x=\"Hours\",y=\"Scores\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since the data available is only a series data (independent variable), we need to convert the independent variable into arrays and reshape it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array(df[\"Hours\"]).reshape(-1, 1)\n",
    "y=df[\"Scores\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data set is small, still i would like to split the data into train and validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### the r squared value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r_sqr value: 0.9529481969048356\n"
     ]
    }
   ],
   "source": [
    "print(f'r_sqr value: {lr.score(X, y)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.25\n"
     ]
    }
   ],
   "source": [
    "to_predict=np.array(float(input())).reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([92.90985477])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(to_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_1 = pickle.dumps(lr)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}