linearRegression_Sol1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Linear Regression with Python Scikit Learn**\n",
    "In this section we will see how the Python Scikit-Learn library for machine learning can be used to implement regression functions. We will start with simple linear regression involving two variables.\n",
    "\n",
    "### **Simple Linear Regression**\n",
    "In this regression task we will predict the percentage of marks that a student is expected to score based upon the number of hours they studied. This is a simple linear regression task as it involves just two variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    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\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    'Hours': [2.5, 5.1, 3.2, 8.5, 3.5, 1.5, 9.2, 5.5, 8.3, 2.7, 7.7, 5.9, 4.5, 3.3, 1.1, 8.9, 2.5, 1.9, 6.1, 7.4, 2.7, 4.8, 3.8, 6.9, 7.8],\n",
    "    'Scores': [21, 47, 27, 75, 30, 20, 88, 60, 81, 25, 85, 62, 41, 42, 17, 95, 30, 24, 67, 69, 30, 54, 35, 76, 86]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the Data into Features and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['Hours']]  # Features\n",
    "y = df['Scores']   # Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the Data into Training and Testing Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
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       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
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       "\n",
       "#sk-container-id-1 div.sk-container {\n",
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       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
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       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
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       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
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       "  flex-direction: column;\n",
       "  align-items: center;\n",
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       "  padding-left: 1em;\n",
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       "\n",
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       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
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       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict the Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the Training Data and the Regression Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train, y_train, color='blue', label='Training data')\n",
    "plt.plot(X_train, model.predict(X_train), color='red', label='Regression line')\n",
    "plt.title('Hours vs Scores (Training data)')\n",
    "plt.xlabel('Hours Studied')\n",
    "plt.ylabel('Scores')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model Coefficients and Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope (coefficient): 9.682078154455697\n",
      "Intercept: 2.826892353899737\n",
      "Predictions: [83.18814104 27.03208774 27.03208774 69.63323162 59.95115347]\n"
     ]
    }
   ],
   "source": [
    "# Coefficients\n",
    "print(\"Slope (coefficient):\", model.coef_[0])\n",
    "print(\"Intercept:\", model.intercept_)\n",
    "\n",
    "# Predictions\n",
    "print(\"Predictions:\", y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What will be predicted score if a student studies for 9.25/hours day? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\anonymous\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "92.38611528261494"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict the score for a student who studies for 9.25 hours per day\n",
    "hours_studied = 9.25\n",
    "predicted_score = model.predict([[hours_studied]])\n",
    "\n",
    "predicted_score[0]\n"
   ]
  }
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