Task-1-INTERNSHIP-Sparks-Foundation-SEPT'22.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abe9180f",
   "metadata": {},
   "source": [
    "# The \"SPARKS FOUNDATION\"- GRIP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb15f60",
   "metadata": {},
   "source": [
    "## Data Science and Business Analytics - SEPT'2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43f11ba3",
   "metadata": {},
   "source": [
    "### By - Ashish Singh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb31a3b",
   "metadata": {},
   "source": [
    "## TASK 1 : Prediction using supervised ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "239a5248",
   "metadata": {},
   "source": [
    "### DATA SET "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8d7db5",
   "metadata": {},
   "source": [
    "Students Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3469d5",
   "metadata": {},
   "source": [
    "## Problem set "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f751d5e4",
   "metadata": {},
   "source": [
    "(1) Predict the percentage of a student based on the no. of study hours."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0cc6046",
   "metadata": {},
   "source": [
    "(2) What will be predicted score if a student studies for 9.25 hrs/ day?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4cc62e3",
   "metadata": {},
   "source": [
    "## Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f282c7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70cb665",
   "metadata": {},
   "source": [
    "### Reading data from remote link"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fe88511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data imported successfully\n"
     ]
    },
    {
     "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",
       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.2</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.5</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.3</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.7</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.7</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.5</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.3</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.9</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.9</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.1</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.4</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.8</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.8</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.9</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7.8</td>\n",
       "      <td>86</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\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"http://bit.ly/w-data\"\n",
    "score_data = pd.read_csv(url)\n",
    "print(\"Data imported successfully\")\n",
    "\n",
    "score_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36659f7e",
   "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",
       "      <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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35ecf64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3818f10f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of     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\n",
       "5     1.5      20\n",
       "6     9.2      88\n",
       "7     5.5      60\n",
       "8     8.3      81\n",
       "9     2.7      25\n",
       "10    7.7      85\n",
       "11    5.9      62\n",
       "12    4.5      41\n",
       "13    3.3      42\n",
       "14    1.1      17\n",
       "15    8.9      95\n",
       "16    2.5      30\n",
       "17    1.9      24\n",
       "18    6.1      67\n",
       "19    7.4      69\n",
       "20    2.7      30\n",
       "21    4.8      54\n",
       "22    3.8      35\n",
       "23    6.9      76\n",
       "24    7.8      86>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_data.info"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d685dc90",
   "metadata": {},
   "source": [
    "### plotting the data to see relation between hours of study and score if any "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4b62172a",
   "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": [
    "score_data.plot(x='Hours', y='Scores', style='o')  \n",
    "plt.title('Hours vs Percentage')  \n",
    "plt.xlabel('Hours Studied')  \n",
    "plt.ylabel('Percentage Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e20b62",
   "metadata": {},
   "source": [
    "#### From the graph above, we can clearly see that there is a positive linear relation between the number of hours studied and percentage of score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8406e92e",
   "metadata": {},
   "source": [
    "### Preparing the Data set for regression into 'inputs' and 'outputs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0bb6770",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = s_data.iloc[:, :-1].values  \n",
    "y = s_data.iloc[:, 1].values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e464d72",
   "metadata": {},
   "source": [
    "split this data into training and test sets. We'll do this by using Scikit-Learn's built-in train_test_split() method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "309debda",
   "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, \n",
    "                            test_size=0.2, random_state=0) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53d75239",
   "metadata": {},
   "source": [
    "### Training the Algorithim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cf38ee6",
   "metadata": {},
   "source": [
    "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": 20,
   "id": "69b1cf49",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression  \n",
    "regressor = LinearRegression() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "19808f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete.\n"
     ]
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train) \n",
    "\n",
    "print(\"Training complete.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e333cb",
   "metadata": {},
   "source": [
    "### Plotting the regression line "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1cb0454d",
   "metadata": {},
   "outputs": [],
   "source": [
    "line = regressor.coef_*X+regressor.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32b7ce19",
   "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(X, y)\n",
    "plt.plot(X, line);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f1dd67",
   "metadata": {},
   "source": [
    "## Making the prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bd5fde03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.5]\n",
      " [3.2]\n",
      " [7.4]\n",
      " [2.5]\n",
      " [5.9]]\n"
     ]
    }
   ],
   "source": [
    "# testing data-in hours\n",
    "print(X_test) \n",
    "# Predicting the score \n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a96c36a8",
   "metadata": {},
   "source": [
    "##### Comparing actual v/s predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dfe6a720",
   "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>0</th>\n",
       "      <td>20</td>\n",
       "      <td>16.884145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>33.732261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69</td>\n",
       "      <td>75.357018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>26.794801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>62</td>\n",
       "      <td>60.491033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      20  16.884145\n",
       "1      27  33.732261\n",
       "2      69  75.357018\n",
       "3      30  26.794801\n",
       "4      62  60.491033"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})  \n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f1b80a",
   "metadata": {},
   "source": [
    "#### test with your own data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "013edbe1",
   "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": [
    "sns.kdeplot(y_pred,label=\"Predicted\", shade=True);\n",
    "\n",
    "sns.kdeplot(data=y_test, label=\"Actual\", shade=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9b5f1867",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A student who studies  9.25  hours is estimated to score  93.69173248737535\n"
     ]
    }
   ],
   "source": [
    "h = [[9.25]]\n",
    "s = regressor.predict(h)\n",
    "print('A student who studies ', h[0][0] , ' hours is estimated to score ', s[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d7a5e2e",
   "metadata": {},
   "source": [
    "### Evaluating the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c735b19",
   "metadata": {},
   "source": [
    "The final step is to evaluate the performance of algorithm. This step is particularly important to compare how well different algorithms perform on a particular dataset. For simplicity here, we have chosen the mean square error. There are many such metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a0d0cfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 4.183859899002975\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', \n",
    "      metrics.mean_absolute_error(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d602fed4",
   "metadata": {},
   "source": [
    "## Here, we come to an end of this task. Thank You."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7625c0a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}