Linear Regression/Linear Regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2021/03/a-practical-tutorial-to-simple-linear-regression-using-python/\n",
    "\n",
    "https://realpython.com/linear-regression-in-python/#what-is-regression\n",
    "\n",
    "https://towardsdatascience.com/linear-regression-using-python-b136c91bf0a2\n",
    "\n",
    "https://analyticsindiamag.com/beginners-guide-to-linear-regression-in-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We’re living in the era of large amounts of data, powerful computers, and artificial intelligence. Data science and machine learning are driving image recognition, autonomous vehicles development, decisions in the financial and energy sectors, advances in medicine, the rise of social networks, and more. Linear regression is an important part of this.\n",
    "\n",
    "Machine Learning (ML): ML is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn themselves and improve from the experience without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn themselves.\n",
    "\n",
    "Supervised Learning: The model is trained using ‘labeled data’. Datasets are said to contain labels that contain both input and output parameters. To simplify – ‘Data is already tagged with the correct answer’.\n",
    "\n",
    "What is Regression?\n",
    "\n",
    ">Regression searches for relationships among variables.Regression analysis is one of the most important fields in statistics and machine learning. There are many regression methods available. Linear regression is one of them. Generally, in regression analysis, you need to find a function that maps some features or variables to others sufficiently well. The dependent features are called the **dependent variables, outputs, or responses**. The independent features are called the **independent variables, inputs, or predictors**. It is a common practice to denote the **outputs with 𝑦 and inputs with 𝑥**. If there are two or more independent variables, they can be represented as the vector 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of inputs.\n",
    "\n",
    "Why do we need Regression?\n",
    "\n",
    ">- We need regression to answer whether and how some phenomenon influences the other or how several variables are related. For example, we can use it to determine if and to what extent the experience or gender impact salaries.\n",
    "- Regression is also useful when you want to forecast a response using a new set of predictors. For example, you could try to predict electricity consumption of a household for the next hour given the outdoor temperature, time of day, and number of residents in that household.\n",
    "\n",
    "Types of Regression:\n",
    "\n",
    "- Simple Linear Regression\n",
    "- Multiple Linear Regression\n",
    "- Polynomial Regression\n",
    "- Advanced Linear Regression\n",
    "\n",
    "Steps involved in performing Regression are:\n",
    "\n",
    "- Importing the packages and classes we need.\n",
    "- Defining data to work with and eventually doing appropriate transformations.\n",
    "- Creating a regression model and fitting it with existing data.\n",
    "- Checking the results of model fitting to know whether the model is satisfactory.\n",
    "- Applying the model for predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. <a href = \"#1.-Important-Terms-and-Concepts\">Important Terms and Concepts</a>\n",
    "2. <a href = \"#2.-Simple-Linear-Regression\">Simple Linear Regression</a>\n",
    "3. <a href = \"#3.-Multiple-Linear-Regression\">Multiple Linear Regression</a>\n",
    "4. <a href = \"#4.-Polynomial-Regression\">Polynomial Regression</a>\n",
    "5. <a href = \"#5.-Advanced-Linear-Regression\">Advanced Linear Regression</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Important Terms and Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When implementing linear regression of some dependent variable 𝑦 on the set of independent variables 𝐱 = (𝑥₁, …, 𝑥ᵣ), where 𝑟 is the number of predictors, you assume a linear relationship between 𝑦 and 𝐱: 𝑦 = 𝛽₀ + 𝛽₁𝑥₁ + ⋯ + 𝛽ᵣ𝑥ᵣ + 𝜀. This equation is the regression equation. **𝛽₀, 𝛽₁, …, 𝛽ᵣ are the regression coefficients**, and **𝜀 is the random error**.\n",
    "\n",
    "Linear regression calculates the **estimators** of the regression coefficients or simply the predicted weights, denoted with 𝑏₀, 𝑏₁, …, 𝑏ᵣ. They define the **estimated regression function 𝑓(𝐱) = 𝑏₀ + 𝑏₁𝑥₁ + ⋯ + 𝑏ᵣ𝑥ᵣ**. This function should capture the dependencies between the inputs and output sufficiently well.\n",
    "\n",
    "The **estimated or predicted response**, 𝑓(𝐱ᵢ), for each observation 𝑖 = 1, …, 𝑛, should be as close as possible to the corresponding **actual response 𝑦ᵢ**. The differences 𝑦ᵢ - 𝑓(𝐱ᵢ) for all observations 𝑖 = 1, …, 𝑛, are called the **residuals**. Regression is about determining the best predicted weights, that is the weights corresponding to the smallest residuals.\n",
    "\n",
    "To get the best weights, you usually **minimize the sum of squared residuals (SSR)** for all observations 𝑖 = 1, …, 𝑛: SSR = Σᵢ(𝑦ᵢ - 𝑓(𝐱ᵢ))². This approach is called the **method of ordinary least squares**.\n",
    "\n",
    "The **coefficient of determination, denoted as 𝑅²**, tells you which amount of variation in 𝑦 can be explained by the dependence on 𝐱 using the particular regression model. **Larger 𝑅² indicates a better fit** and means that the model can better explain the variation of the output with different inputs. The value of r-square always increases as new variables(attributes) are added to the model, no matter that the newly added attributes have a positive impact on the model or not. also, it can lead to over fitting of the model if there are large no. of variables.\n",
    "\n",
    "The value 𝑅² = 1 corresponds to SSR = 0, that is to the **perfect fit** since the values of predicted and actual responses fit completely to each other.\n",
    "\n",
    "**Adjusted r-square** is a modified form of r-square whose value increases if new predictors tend to improve model’s performance and decreases if new predictors does not improve performance as expected.\n",
    "\n",
    "**Models**: Those are output by algorithms and are comprised of model data and a prediction algorithm.\n",
    "\n",
    "**Training Model**: In supervised learning, an ML Algorithm builds a model by examining many examples and attempting to find a model that minimizes loss and improves prediction accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<a href=\"#Content\">Back to Content</a>]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple or single-variate linear regression is the simplest case of linear regression with a single independent variable, 𝐱 = 𝑥.\n",
    "\n",
    "The following figure illustrates simple linear regression:"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">When implementing simple linear regression, we typically start with a given set of input-output (𝑥-𝑦) pairs (green circles). These pairs are our observations. For example, the leftmost observation (green circle) has the input 𝑥 = 5 and the actual output (response) 𝑦 = 5. The estimated regression function (black line) has the equation 𝑓(𝑥) = 𝑏₀ + 𝑏₁𝑥. Our goal is to calculate the optimal values of the predicted weights 𝑏₀ and 𝑏₁ that minimize SSR and determine the estimated regression function. The value of 𝑏₀, also called the **intercept**, shows the point where the estimated regression line crosses the 𝑦 axis. It is the value of the estimated response 𝑓(𝑥) for 𝑥 = 0. The value of **𝑏₁ determines the slope of the estimated regression line**.The predicted responses (red squares) are the points on the regression line that correspond to the input values. For example, for the input 𝑥 = 5, the predicted response is 𝑓(5) = 8.33 (represented with the leftmost red square).The residuals (vertical dashed gray lines) can be calculated as 𝑦ᵢ - 𝑓(𝐱ᵢ) = 𝑦ᵢ - 𝑏₀ - 𝑏₁𝑥ᵢ for 𝑖 = 1, …, 𝑛. They are the distances between the green circles and red squares. When we implement linear regression, we are actually trying to minimize these distances and make the red squares as close to the predefined green circles as possible."
   ]
  },
  {
   "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>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.767874</td>\n",
       "      <td>59.627484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.107671</td>\n",
       "      <td>36.146453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.266917</td>\n",
       "      <td>46.912878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.815180</td>\n",
       "      <td>29.125660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.753758</td>\n",
       "      <td>68.170414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age     Height\n",
       "0  14.767874  59.627484\n",
       "1   3.107671  36.146453\n",
       "2   7.266917  46.912878\n",
       "3   1.815180  29.125660\n",
       "4  16.753758  68.170414"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading Dataset from pkl file\n",
    "basepath = '../../Data\\\\'\n",
    "df = pd.read_pickle(basepath+ 'AgesAndHeights.pkl')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age       0\n",
       "Height    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = df[df['Age']>0]\n",
    "clean_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bf6c93e700>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of Actual Data\n",
    "ages = clean_df[['Age']]\n",
    "heights = clean_df['Height']\n",
    "plt.scatter(ages,heights, label = 'Actual Data')\n",
    "plt.title('Height Vs Age')\n",
    "plt.xlabel('Age(Yrs)')\n",
    "plt.ylabel('Height(Inches)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Building model\n",
    "model = LinearRegression()\n",
    "model.fit(ages, heights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.954958596651655\n",
      "adjusted_r_squared: 0.9544636361752995\n",
      "intercept: 29.961857614615845\n",
      "slope: [2.0014169]\n"
     ]
    }
   ],
   "source": [
    "#Result\n",
    "y = heights\n",
    "X = ages\n",
    "r_sq = model.score(ages, heights)\n",
    "adjusted_r_squared = 1 - (1-r_sq)*(len(y)-1)/(len(y)-X.shape[1]-1)\n",
    "print('coefficient of determination:', r_sq)\n",
    "print('adjusted_r_squared:', adjusted_r_squared)\n",
    "print('intercept:', model.intercept_)\n",
    "print('slope:', model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It shows that the predictor variables/independant variables explains the variance of response variable/dependant variable by 95.49% in the simple linear regression model whereas in case of adjusted R-Square, it shows that the predictor variables tends to improve the model performance by 94.98%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response: [59.51852983 36.18160357 44.50598813 33.59478891 63.49311126 40.67196595\n",
      " 47.28555165 38.37980295 44.68566341 33.30057763 46.56753117 59.48172578\n",
      " 60.55441984 63.29614048 31.68145078 46.84046677 53.82647578 31.49951265\n",
      " 52.70200153 32.55565241 50.60059065 64.77320768 50.596527   42.44598738\n",
      " 61.08695501 35.51271584 61.17993762 45.12839042 34.04806453 49.78919876\n",
      " 35.71097799 33.04868164 40.89364203 60.07042681 41.95780206 39.57924823\n",
      " 47.87437881 40.00939044 49.76698376 40.37485961 30.35350605 31.74746959\n",
      " 44.59180672 51.68638204 61.38163107 31.58734001 60.78657484 48.40691423\n",
      " 47.94489626 37.45456019 31.46677323 43.16389776 49.47792764 44.77423534\n",
      " 64.64065989 51.04492208 60.3196813  48.91471277 57.07488319 45.57555718\n",
      " 35.00363381 39.40187117 50.05340038 53.52104581 47.04871473 35.13263367\n",
      " 32.83990112 50.78944559 63.70441223 54.42848169 49.00487902 50.13987434\n",
      " 46.36590136 50.97047289 44.51953556 52.23684336 33.56359501 45.55263839\n",
      " 44.03047717 41.68852089 64.67267302 43.11442964 60.66133977 65.76290681\n",
      " 58.53068259 58.23979145 52.15781342 57.47707214 44.61965789 41.15279894\n",
      " 35.21678947 41.00826374 56.21527052]\n"
     ]
    }
   ],
   "source": [
    "#Prediction\n",
    "y_pred = model.predict(ages)\n",
    "print('predicted response:', y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1bf6c988cd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualization of Predicted Data\n",
    "plt.scatter(ages,y_pred, label = 'Predicted Data')\n",
    "plt.title('Height Vs Age')\n",
    "plt.xlabel('Age(Yrs)')\n",
    "plt.ylabel('Height(Inches)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response: [59.9831111  63.9859449  69.99019559]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\program files\\python39\\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": [
    "#Prediction for new data\n",
    "age_new = np.array([15,17,20]).reshape((-1, 1))\n",
    "y_pred = model.predict(age_new)\n",
    "print('predicted response:', y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<a href=\"#Content\">Back to Content</a>]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple or multivariate linear regression is a case of linear regression with two or more independent variables.\n",
    "\n",
    "If there are just two independent variables, the estimated regression function is 𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂. It represents a regression plane in a three-dimensional space. The goal of regression is to determine the values of the weights 𝑏₀, 𝑏₁, and 𝑏₂ such that this plane is as close as possible to the actual responses and yield the minimal SSR.\n",
    "\n",
    "The case of more than two independent variables is similar, but more general. The estimated regression function is 𝑓(𝑥₁, …, 𝑥ᵣ) = 𝑏₀ + 𝑏₁𝑥₁ + ⋯ +𝑏ᵣ𝑥ᵣ, and there are 𝑟 + 1 weights to be determined when the number of inputs is 𝑟."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv(basepath + '50_Startups.csv')\n",
    "#Dividing dataset into Input X and Output y\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, 4]\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Florida  New York\n",
       "0        0         1\n",
       "1        0         0\n",
       "2        1         0\n",
       "3        0         1\n",
       "4        1         0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert categorical columns\n",
    "states=pd.get_dummies(X['State'],drop_first=True)\n",
    "states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  Florida  New York\n",
       "0  165349.20       136897.80        471784.10        0         1\n",
       "1  162597.70       151377.59        443898.53        0         0\n",
       "2  153441.51       101145.55        407934.54        1         0\n",
       "3  144372.41       118671.85        383199.62        0         1\n",
       "4  142107.34        91391.77        366168.42        1         0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the state coulmn\n",
    "X=X.drop('State',axis=1)\n",
    "# Concat the dummy variables\n",
    "X=pd.concat([X,states],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>8.451019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.950277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing Spend</td>\n",
       "      <td>8.092278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>2.004519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.928836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variables       VIF\n",
       "0        R&D Spend  8.451019\n",
       "1   Administration  4.950277\n",
       "2  Marketing Spend  8.092278\n",
       "3          Florida  2.004519\n",
       "4         New York  1.928836"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Interpretation:\n",
    "- VIF starts at 1 and has no upper limit.\n",
    "- VIF = 1, no correlation between the independent variable and the other variables.\n",
    "- VIF exceeding 5 or 10 indicates high multicollinearity between this independent variable and the others.\n",
    "- 'R&D Spend' and 'Marketing Spend' have values greater than 5 which means they have high multicollinearity between this independent variable and the others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the \"Marketing Spend\" coulmn\n",
    "X=X.drop('Marketing Spend',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>3.917998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.863405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>1.881915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.909248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variables       VIF\n",
       "0       R&D Spend  3.917998\n",
       "1  Administration  4.863405\n",
       "2         Florida  1.881915\n",
       "3        New York  1.909248"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity after removing high correlated column\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fitting Multiple Linear Regression to the Training set\n",
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square: 0.9477002402858664\n",
      "adjusted_r_squared: 0.9430513727557213\n",
      "intercept: 48444.52707097882\n",
      "Coefficients: [ 8.51613579e-01 -1.69679775e-03  4.84732966e+02  1.73195428e+02]\n"
     ]
    }
   ],
   "source": [
    "#Result\n",
    "r_sq=r2_score(y_test,y_pred)\n",
    "adjusted_r_squared = 1 - (1-r_sq)*(len(y)-1)/(len(y)-X.shape[1]-1)\n",
    "print('R-Square:', r_sq)\n",
    "print('adjusted_r_squared:', adjusted_r_squared)\n",
    "print('intercept:', regressor.intercept_)\n",
    "print('Coefficients:', regressor.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It shows that the predictor variables/independant variables explains the variance of response variable/dependant variable by 94.77% in the simple linear regression model whereas in case of adjusted R-Square, it shows that the predictor variables tends to improve the model performance by 94.30%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<a href=\"#Content\">Back to Content</a>]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Polynomial regression has a single independent variable, and the estimated regression function is a polynomial of degree 2: 𝑓(𝑥) = 𝑏₀ + 𝑏₁𝑥 + 𝑏₂𝑥². Keeping this in mind, compare the previous regression function with the function 𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂ used for linear regression. They look very similar and are both linear functions of the unknowns 𝑏₀, 𝑏₁, and 𝑏₂. This is why you can solve the polynomial regression problem as a linear problem with the term 𝑥² regarded as an input variable.\n",
    "\n",
    "In the case of two variables and the polynomial of degree 2, the regression function has this form: 𝑓(𝑥₁, 𝑥₂) = 𝑏₀ + 𝑏₁𝑥₁ + 𝑏₂𝑥₂ + 𝑏₃𝑥₁² + 𝑏₄𝑥₁𝑥₂ + 𝑏₅𝑥₂². The procedure for solving the problem is identical to the previous case. The choice of the optimal degree of the polynomial regression function has no straightforward rule, it depends on the case. However, we should be aware of two problems that might follow the choice of the degree: underfitting and overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Underfitting** occurs when a model can’t accurately capture the dependencies among data, usually as a consequence of its own simplicity. It often yields a low 𝑅² with known data and bad generalization capabilities when applied with new data.\n",
    "\n",
    "**Overfitting** happens when a model learns both dependencies among data and random fluctuations. In other words, a model learns the existing data too well. Complex models, which have many features or terms, are often prone to overfitting. When applied to known data, such models usually yield high 𝑅². However, they often don’t generalize well and have significantly lower 𝑅² when used with new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv(basepath + '50_Startups.csv')\n",
    "#Dividing dataset into Input X and Output y\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, 4]\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Florida  New York\n",
       "0        0         1\n",
       "1        0         0\n",
       "2        1         0\n",
       "3        0         1\n",
       "4        1         0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert categorical columns\n",
    "states=pd.get_dummies(X['State'],drop_first=True)\n",
    "states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  Florida  New York\n",
       "0  165349.20       136897.80        471784.10        0         1\n",
       "1  162597.70       151377.59        443898.53        0         0\n",
       "2  153441.51       101145.55        407934.54        1         0\n",
       "3  144372.41       118671.85        383199.62        0         1\n",
       "4  142107.34        91391.77        366168.42        1         0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the state coulmn\n",
    "X=X.drop('State',axis=1)\n",
    "# Concat the dummy variables\n",
    "X=pd.concat([X,states],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>8.451019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.950277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing Spend</td>\n",
       "      <td>8.092278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>2.004519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.928836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variables       VIF\n",
       "0        R&D Spend  8.451019\n",
       "1   Administration  4.950277\n",
       "2  Marketing Spend  8.092278\n",
       "3          Florida  2.004519\n",
       "4         New York  1.928836"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the \"Marketing Spend\" coulmn\n",
    "X=X.drop('Marketing Spend',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>3.917998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.863405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>1.881915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.909248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variables       VIF\n",
       "0       R&D Spend  3.917998\n",
       "1  Administration  4.863405\n",
       "2         Florida  1.881915\n",
       "3        New York  1.909248"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity after removing high correlated column\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to include 𝑥² (and perhaps other terms) as additional features when implementing polynomial regression. For that reason, we should transform the input dataframe to contain the additional column(s) with the values of 𝑥² (and eventually more features).The variable transformer refers to an instance of PolynomialFeatures which you can use to transform the input x.\n",
    "\n",
    "You can provide several optional parameters to PolynomialFeatures:\n",
    "\n",
    "- degree is an integer (2 by default) that represents the degree of the polynomial regression function.\n",
    "- interaction_only is a Boolean (False by default) that decides whether to include only interaction features (True) or all features (False).\n",
    "- include_bias is a Boolean (True by default) that decides whether to include the bias (intercept) column of ones (True) or not (False)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [7.53288700e+04, 1.44135980e+05, 1.00000000e+00, 0.00000000e+00,\n",
       "        5.67443866e+09, 1.08576005e+10, 7.53288700e+04, 0.00000000e+00,\n",
       "        2.07751807e+10, 1.44135980e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [7.21076000e+04, 1.27864550e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        5.19950598e+09, 9.22000583e+09, 0.00000000e+00, 7.21076000e+04,\n",
       "        1.63493431e+10, 0.00000000e+00, 1.27864550e+05, 0.00000000e+00,\n",
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       "       [6.60515200e+04, 1.82645560e+05, 1.00000000e+00, 0.00000000e+00,\n",
       "        4.36280329e+09, 1.20640169e+10, 6.60515200e+04, 0.00000000e+00,\n",
       "        3.33594006e+10, 1.82645560e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
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       "       [6.19944800e+04, 1.15641280e+05, 1.00000000e+00, 0.00000000e+00,\n",
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       "        0.00000000e+00, 0.00000000e+00],\n",
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       "       [6.34088600e+04, 1.29219610e+05, 0.00000000e+00, 0.00000000e+00,\n",
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       "        1.66977076e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
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       "        1.06208462e+10, 1.03057490e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [4.64260700e+04, 1.57693920e+05, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.15537998e+09, 7.32110897e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.48673724e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [4.60140200e+04, 8.50474400e+04, 0.00000000e+00, 1.00000000e+00,\n",
       "        2.11729004e+09, 3.91337461e+09, 0.00000000e+00, 4.60140200e+04,\n",
       "        7.23306705e+09, 0.00000000e+00, 8.50474400e+04, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.00000000e+00],\n",
       "       [2.86637600e+04, 1.27056210e+05, 1.00000000e+00, 0.00000000e+00,\n",
       "        8.21611137e+08, 3.64190871e+09, 2.86637600e+04, 0.00000000e+00,\n",
       "        1.61432805e+10, 1.27056210e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [4.40699500e+04, 5.12831400e+04, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.94216049e+09, 2.26004542e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.62996045e+09, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [2.02295900e+04, 6.59479300e+04, 0.00000000e+00, 1.00000000e+00,\n",
       "        4.09236312e+08, 1.33409959e+09, 0.00000000e+00, 2.02295900e+04,\n",
       "        4.34912947e+09, 0.00000000e+00, 6.59479300e+04, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.00000000e+00],\n",
       "       [3.85585100e+04, 8.29820900e+04, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.48675869e+09, 3.19966575e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        6.88602726e+09, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [2.87543300e+04, 1.18546050e+05, 0.00000000e+00, 0.00000000e+00,\n",
       "        8.26811494e+08, 3.40871224e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.40531660e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [2.78929200e+04, 8.47107700e+04, 1.00000000e+00, 0.00000000e+00,\n",
       "        7.78014986e+08, 2.36283073e+09, 2.78929200e+04, 0.00000000e+00,\n",
       "        7.17591455e+09, 8.47107700e+04, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [2.36409300e+04, 9.61896300e+04, 0.00000000e+00, 0.00000000e+00,\n",
       "        5.58893571e+08, 2.27401231e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        9.25244492e+09, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [1.55057300e+04, 1.27382300e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        2.40427663e+08, 1.97515555e+09, 0.00000000e+00, 1.55057300e+04,\n",
       "        1.62262504e+10, 0.00000000e+00, 1.27382300e+05, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.00000000e+00],\n",
       "       [2.21777400e+04, 1.54806140e+05, 0.00000000e+00, 0.00000000e+00,\n",
       "        4.91852152e+08, 3.43325032e+09, 0.00000000e+00, 0.00000000e+00,\n",
       "        2.39649410e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [1.00023000e+03, 1.24153040e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        1.00046005e+06, 1.24181595e+08, 0.00000000e+00, 1.00023000e+03,\n",
       "        1.54139773e+10, 0.00000000e+00, 1.24153040e+05, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.00000000e+00],\n",
       "       [1.31546000e+03, 1.15816210e+05, 1.00000000e+00, 0.00000000e+00,\n",
       "        1.73043501e+06, 1.52351592e+08, 1.31546000e+03, 0.00000000e+00,\n",
       "        1.34133945e+10, 1.15816210e+05, 0.00000000e+00, 1.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 1.35426920e+05, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.83404507e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00],\n",
       "       [5.42050000e+02, 5.17431500e+04, 0.00000000e+00, 1.00000000e+00,\n",
       "        2.93818202e+05, 2.80473745e+07, 0.00000000e+00, 5.42050000e+02,\n",
       "        2.67735357e+09, 0.00000000e+00, 5.17431500e+04, 0.00000000e+00,\n",
       "        0.00000000e+00, 1.00000000e+00],\n",
       "       [0.00000000e+00, 1.16983800e+05, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        1.36852095e+10, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transformer = PolynomialFeatures(degree=2, include_bias=False)\n",
    "transformer.fit(X)\n",
    "X_ = transformer.transform(X)\n",
    "X_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating and fitting the model\n",
    "model = LinearRegression()\n",
    "model = model.fit(X_,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Square: 0.9557782304153424\n",
      "adjusted_r_squared: 0.9518474064522617\n",
      "intercept: 35748.96660649995\n",
      "Coefficients: [ 1.23008241e+00  1.80313548e-02  8.50177765e+03  4.11403084e+03\n",
      " -1.15290550e-08 -2.22657329e-06 -1.60026531e-01 -1.51651598e-01\n",
      "  2.89226357e-07 -4.03609463e-02  2.39823848e-02  8.50177688e+03\n",
      "  0.00000000e+00  4.11403010e+03]\n"
     ]
    }
   ],
   "source": [
    "#Result\n",
    "r_sq = model.score(X_,y)\n",
    "adjusted_r_squared = 1 - (1-r_sq)*(len(y)-1)/(len(y)-X.shape[1]-1)\n",
    "print('R-Square:', r_sq)\n",
    "print('adjusted_r_squared:', adjusted_r_squared)\n",
    "print('intercept:', model.intercept_)\n",
    "print('Coefficients:', model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">It shows that the predictor variables/independant variables explains the variance of response variable/dependant variable by 95.57% in the simple linear regression model whereas in case of adjusted R-Square, it shows that the predictor variables tends to improve the model performance by 95.18%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "[182750.88906111 190005.85568697 182816.18321678 170343.65419484\n",
      " 176040.01108004 163762.82523936 165929.07088513 152638.02373466\n",
      " 146535.74108422 162816.55254998 137657.81269512 142983.73231828\n",
      " 128326.62680261 128809.50581532 142717.1541076  145564.97694639\n",
      " 116988.68706637 127560.95866713 128728.54479126 120815.21602891\n",
      " 115950.74485546 114903.92360971 113257.29682373 109935.2709426\n",
      " 116986.11821505 103299.75596834 111908.18907955 111251.84962466\n",
      " 102089.63855326 105526.94215861 104368.76969805 102238.52221019\n",
      " 102616.35406976 100135.3184427   86566.75783424  90527.2932102\n",
      "  77137.84578364  86589.44706371  66846.65404693  79525.58021742\n",
      "  69721.97391637  77513.42519897  64170.04034696  66343.15002238\n",
      "  65102.01731469  54453.45742426  55114.27346645  43495.43916747\n",
      "  47447.42248546  41816.46627711]\n"
     ]
    }
   ],
   "source": [
    "#Prediction\n",
    "y_pred = model.predict(X_)\n",
    "print('predicted response:', y_pred, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<a href=\"#Content\">Back to Content</a>]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Advanced Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can implement linear regression in Python relatively easily by using the package **Statsmodels** as well. Typically, this is desirable when there is a need for more detailed results.\n",
    "\n",
    "**Statsmodel** is a Python library designed for more statistically-oriented approaches to data analysis, with an emphasis on econometric analyses. It integrates well with the pandas and numpy libraries we covered in a previous post. It also has built in support for many of the statistical tests to check the quality of the fit and a dedicated set of plotting functions to visualize and diagnose the fit. Scikit-learn also has support for linear regression, including many forms of regularized regression lacking in statsmodels, but it lacks the rich set of statistical tests and diagnostics that have been developed for linear models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R&amp;D Spend</th>\n",
       "      <th>Administration</th>\n",
       "      <th>Marketing Spend</th>\n",
       "      <th>State</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>165349.20</td>\n",
       "      <td>136897.80</td>\n",
       "      <td>471784.10</td>\n",
       "      <td>New York</td>\n",
       "      <td>192261.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162597.70</td>\n",
       "      <td>151377.59</td>\n",
       "      <td>443898.53</td>\n",
       "      <td>California</td>\n",
       "      <td>191792.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>153441.51</td>\n",
       "      <td>101145.55</td>\n",
       "      <td>407934.54</td>\n",
       "      <td>Florida</td>\n",
       "      <td>191050.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>New York</td>\n",
       "      <td>182901.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>Florida</td>\n",
       "      <td>166187.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
       "0  165349.20       136897.80        471784.10    New York  192261.83\n",
       "1  162597.70       151377.59        443898.53  California  191792.06\n",
       "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
       "3  144372.41       118671.85        383199.62    New York  182901.99\n",
       "4  142107.34        91391.77        366168.42     Florida  166187.94"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the dataset\n",
    "dataset = pd.read_csv(basepath + '50_Startups.csv')\n",
    "#Dividing dataset into Input X and Output y\n",
    "X = dataset.iloc[:, :-1]\n",
    "y = dataset.iloc[:, 4]\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Florida</th>\n",
       "      <th>New York</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "   Florida  New York\n",
       "0        0         1\n",
       "1        0         0\n",
       "2        1         0\n",
       "3        0         1\n",
       "4        1         0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert categorical columns\n",
    "states=pd.get_dummies(X['State'],drop_first=True)\n",
    "states.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
       "      <td>144372.41</td>\n",
       "      <td>118671.85</td>\n",
       "      <td>383199.62</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142107.34</td>\n",
       "      <td>91391.77</td>\n",
       "      <td>366168.42</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   R&D Spend  Administration  Marketing Spend  Florida  New York\n",
       "0  165349.20       136897.80        471784.10        0         1\n",
       "1  162597.70       151377.59        443898.53        0         0\n",
       "2  153441.51       101145.55        407934.54        1         0\n",
       "3  144372.41       118671.85        383199.62        0         1\n",
       "4  142107.34        91391.77        366168.42        1         0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the state coulmn\n",
    "X=X.drop('State',axis=1)\n",
    "# Concat the dummy variables\n",
    "X=pd.concat([X,states],axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>8.451019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.950277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Marketing Spend</td>\n",
       "      <td>8.092278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>2.004519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.928836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variables       VIF\n",
       "0        R&D Spend  8.451019\n",
       "1   Administration  4.950277\n",
       "2  Marketing Spend  8.092278\n",
       "3          Florida  2.004519\n",
       "4         New York  1.928836"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the \"Marketing Spend\" coulmn\n",
    "X=X.drop('Marketing Spend',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>variables</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R&amp;D Spend</td>\n",
       "      <td>3.917998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Administration</td>\n",
       "      <td>4.863405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Florida</td>\n",
       "      <td>1.881915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New York</td>\n",
       "      <td>1.909248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variables       VIF\n",
       "0       R&D Spend  3.917998\n",
       "1  Administration  4.863405\n",
       "2         Florida  1.881915\n",
       "3        New York  1.909248"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking Multicolinearity after removing high correlated column\n",
    "def calc_vif(X):\n",
    "\n",
    "    # Calculating VIF\n",
    "    vif = pd.DataFrame()\n",
    "    vif[\"variables\"] = X.columns\n",
    "    vif[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "\n",
    "    return(vif)\n",
    "\n",
    "chk_df = X\n",
    "calc_vif(chk_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adds a constant term to the predictor\n",
    "X = sm.add_constant(X)\n",
    "#Performing regression of the predictor on response variables\n",
    "model = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fitting the model\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                 Profit   R-squared:                       0.948\n",
      "Model:                            OLS   Adj. R-squared:                  0.943\n",
      "Method:                 Least Squares   F-statistic:                     205.0\n",
      "Date:                Tue, 04 Oct 2022   Prob (F-statistic):           2.90e-28\n",
      "Time:                        13:23:38   Log-Likelihood:                -526.75\n",
      "No. Observations:                  50   AIC:                             1064.\n",
      "Df Residuals:                      45   BIC:                             1073.\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const            5.46e+04   6371.060      8.571      0.000    4.18e+04    6.74e+04\n",
      "R&D Spend          0.8609      0.031     27.665      0.000       0.798       0.924\n",
      "Administration    -0.0527      0.050     -1.045      0.301      -0.154       0.049\n",
      "Florida         1091.1075   3377.087      0.323      0.748   -5710.695    7892.910\n",
      "New York         -39.3434   3309.047     -0.012      0.991   -6704.106    6625.420\n",
      "==============================================================================\n",
      "Omnibus:                       14.275   Durbin-Watson:                   1.197\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               19.260\n",
      "Skew:                          -0.953   Prob(JB):                     6.57e-05\n",
      "Kurtosis:                       5.369   Cond. No.                     7.06e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 7.06e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(results.summary())"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficient of determination: 0.9479799298023537\n",
      "adjusted coefficient of determination: 0.9433559235625629\n",
      "regression coefficients: const             54604.815108\n",
      "R&D Spend             0.860907\n",
      "Administration       -0.052704\n",
      "Florida            1091.107518\n",
      "New York            -39.343362\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Result\n",
    "print('coefficient of determination:', results.rsquared)\n",
    "print('adjusted coefficient of determination:', results.rsquared_adj)\n",
    "print('regression coefficients:', results.params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[<a href=\"#Content\">Back to Content</a>]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tested :no errors"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}