Task -1 Data science internship.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32d90c4a",
   "metadata": {},
   "source": [
    "### THE SPARKS FOUNDATION DATA SCIENCE AND BUSINESS ANALYTICS INTERNSHIP \n",
    "\n",
    "### TASK-1: Prediction using Supervised Machane Learning (Simple Linear Regression)\n",
    "\n",
    "#### Author: Ngondzana Tikum Michael"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0334b6df",
   "metadata": {},
   "source": [
    "#### In this regression task we will predict the percentage of marks that a student is expected to score based upon the fact that he studies for 9.25 hours."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "230e0823",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing important libraries\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1856e9b",
   "metadata": {},
   "source": [
    "##### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f4adf70d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the data\n",
    "url = 'http://bit.ly/w-data'\n",
    "data = pd.read_csv(url)\n",
    "\n",
    "# Viewing the first few rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "2c9111b7",
   "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",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Hours   25 non-null     float64\n",
      " 1   Scores  25 non-null     int64  \n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 528.0 bytes\n"
     ]
    }
   ],
   "source": [
    "# Knowing our data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b01cd2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "    }\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",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.012000</td>\n",
       "      <td>51.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.525094</td>\n",
       "      <td>25.286887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.700000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.800000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\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": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "87dd7fed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distribution of scores\n",
    "\n",
    "data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours against Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05fc114b",
   "metadata": {},
   "source": [
    "#### From the visualisation, it can be clearly seen that there is a positive linear relation between the number of hours studied and percentage of score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68457915",
   "metadata": {},
   "source": [
    "### Preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "99366c19",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hours\n",
       "0    2.5\n",
       "1    5.1\n",
       "2    3.2\n",
       "3    8.5\n",
       "4    3.5"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing the x and y variables and displaying the x variable\n",
    "\n",
    "X = data[['Hours']] \n",
    "y = data['Scores']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd326c84",
   "metadata": {},
   "source": [
    "### Since we've split aur data into x and y, now we can pass them into the train_test_split function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a7738b59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22    3.8\n",
       "17    1.9\n",
       "24    7.8\n",
       "23    6.9\n",
       "14    1.1\n",
       "1     5.1\n",
       "10    7.7\n",
       "13    3.3\n",
       "8     8.3\n",
       "6     9.2\n",
       "18    6.1\n",
       "4     3.5\n",
       "9     2.7\n",
       "7     5.5\n",
       "20    2.7\n",
       "3     8.5\n",
       "0     2.5\n",
       "21    4.8\n",
       "15    8.9\n",
       "12    4.5\n",
       "Name: Hours, dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                            test_size=0.2, random_state=0) \n",
    "# print the data\n",
    "x_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efefcfb5",
   "metadata": {},
   "source": [
    "### Training the Algorithm\n",
    "#### We have split our data into training and testing sets, and now is finally the time to train our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e36bec28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:\t [9.91065648]\n",
      "Intercept:\t 2.018160041434683\n",
      "Training completed.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "crf = LinearRegression()  \n",
    "crf.fit(X_train, y_train) \n",
    "\n",
    "print(\"Coefficient:\\t\", crf.coef_)\n",
    "print(\"Intercept:\\t\", crf.intercept_)\n",
    "\n",
    "print(\"Training completed.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "0a993685",
   "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": [
    "# Plotting the regression line\n",
    "line = crf.coef_*X+crf.intercept_\n",
    "\n",
    "# Plotting for the test data\n",
    "plt.scatter(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14eb005",
   "metadata": {},
   "source": [
    "### Making Predictions\n",
    "#### Now that we have trained our algorithm, it's time to make some predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bad8359a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Hours\n",
      "5     1.5\n",
      "2     3.2\n",
      "19    7.4\n",
      "16    2.5\n",
      "11    5.9\n"
     ]
    }
   ],
   "source": [
    "# Testing data - In Hours\n",
    "print(X_test) \n",
    "\n",
    " # Predicting the scores\n",
    "y_pred = crf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "26d19829",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "5       20  16.884145\n",
       "2       27  33.732261\n",
       "19      69  75.357018\n",
       "16      30  26.794801\n",
       "11      62  60.491033"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing Actual vs Predicted\n",
    "df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ee0cdbbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Score = 93.69173248737538\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ngondzana Michael\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Now we can also test with your own data\n",
    "\n",
    "hours = [[9.25]]\n",
    "own_pred = crf.predict(hours)\n",
    "print(\"Predicted Score = {}\".format(own_pred[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b890f9",
   "metadata": {},
   "source": [
    "#### With 9.25 hours of study per day, a student is expected to achieve a score of 93.69."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b32aa6",
   "metadata": {},
   "source": [
    "### Model Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d2939268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9515510725211552"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model accuracy\n",
    "crf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8ecdef",
   "metadata": {},
   "source": [
    "### This implies our mode is accurate up to 95% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5e9d205c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics \n",
    "print(\"MAE\",metrics.mean_absolute_error(y_test,y_pred))"
   ]
  }
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