polynomial_regression.ipynb

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  {
   "source": [
    "# Polynomial Regressors"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Libraries Import "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import matplotlib.pyplot as pyplot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures"
   ]
  },
  {
   "source": [
    "## Configuration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SAMPLES_TO_SHOW = 5\n",
    "TRAIN_SPLIT_RATIO = 0.8\n",
    "MIN_POLYNOMIAL_DEGREE = 2\n",
    "MAX_POLYNOMIAL_DEGREE = 5"
   ]
  },
  {
   "source": [
    "## Dataset Creation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "boston_data = load_boston()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 3,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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      "The features of the dataset are:\n\t- CRIM\n\t- ZN\n\t- INDUS\n\t- CHAS\n\t- NOX\n\t- RM\n\t- AGE\n\t- DIS\n\t- RAD\n\t- TAX\n\t- PTRATIO\n\t- B\n\t- LSTAT\n\nThe description of the dataset is: \n\n.. _boston_dataset:\n\nBoston house prices dataset\n---------------------------\n\n**Data Set Characteristics:**  \n\n    :Number of Instances: 506 \n\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n\n    :Attribute Information (in order):\n        - CRIM     per capita crime rate by town\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\n        - INDUS    proportion of non-retail business acres per town\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n        - NOX      nitric oxides concentration (parts per 10 million)\n        - RM       average number of rooms per dwelling\n        - AGE      proportion of owner-occupied units built prior to 1940\n        - DIS      weighted distances to five Boston employment centres\n        - RAD      index of accessibility to radial highways\n        - TAX      full-value property-tax rate per $10,000\n        - PTRATIO  pupil-teacher ratio by town\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n        - LSTAT    % lower status of the population\n        - MEDV     Median value of owner-occupied homes in $1000's\n\n    :Missing Attribute Values: None\n\n    :Creator: Harrison, D. and Rubinfeld, D.L.\n\nThis is a copy of UCI ML housing dataset.\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n\n\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\nprices and the demand for clean air', J. Environ. Economics & Management,\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\npages 244-261 of the latter.\n\nThe Boston house-price data has been used in many machine learning papers that address regression\nproblems.   \n     \n.. topic:: References\n\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n\n"
     ]
    }
   ],
   "source": [
    "feature_names = boston_data[\"feature_names\"]\n",
    "print(\"The features of the dataset are:\")\n",
    "for feature in feature_names:\n",
    "    print(\"\\t- {}\".format(feature))\n",
    "print()\n",
    "\n",
    "print(\"The description of the dataset is: \\n\\n{}\".format(boston_data[\"DESCR\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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     "text": [
      "Random 5 entries are:\n\n        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n493  0.17331  0.0   9.69   0.0  0.585  5.707  54.0  2.3817   6.0  391.0   \n366  3.69695  0.0  18.10   0.0  0.718  4.963  91.4  1.7523  24.0  666.0   \n335  0.03961  0.0   5.19   0.0  0.515  6.037  34.5  5.9853   5.0  224.0   \n462  6.65492  0.0  18.10   0.0  0.713  6.317  83.0  2.7344  24.0  666.0   \n421  7.02259  0.0  18.10   0.0  0.718  6.006  95.3  1.8746  24.0  666.0   \n\n     PTRATIO       B  LSTAT  MEDV  \n493     19.2  396.90  12.01  21.8  \n366     20.2  316.03  14.00  21.9  \n335     20.2  396.90   8.01  21.1  \n462     20.2  396.90  13.99  19.5  \n421     20.2  319.98  15.70  14.2  \n"
     ]
    }
   ],
   "source": [
    "data = boston_data[\"data\"]\n",
    "target = boston_data[\"target\"]\n",
    "target_names = boston_data[\"target\"]\n",
    "\n",
    "boston_df = pd.DataFrame(data, columns=feature_names)\n",
    "boston_df[\"MEDV\"] = target\n",
    "\n",
    "print(\"Random {} entries are:\\n\\n{}\".format(RANDOM_SAMPLES_TO_SHOW, boston_df.sample(n=RANDOM_SAMPLES_TO_SHOW)))"
   ]
  },
  {
   "source": [
    "## Dataset Exploration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "boston_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "[]"
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     "metadata": {},
     "execution_count": 7
    },
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     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot = sns.distplot(boston_df[\"MEDV\"])\n",
    "plot.set_xlabel(\"Value of Owner-occupied Homes in $1000's\")\n",
    "plot.set_title(\"Owner-occupied Homes Histogram\")\n",
    "pyplot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot = sns.heatmap(boston_df.corr().round(1), annot=True)\n",
    "plot.set_title(\"Features Correlations\")\n",
    "pyplot.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for feature in [\"NOX\", \"DIS\"]:\n",
    "    plot = sns.scatterplot(data=boston_df, x=feature, y=\"MEDV\")\n",
    "    plot.set_xlabel(\"Nitric Oxides Concentration\")\n",
    "    plot.set_ylabel(\"Value of Owner-occupied Homes in $1000's\")\n",
    "    plot.set_title(\"Concentration - Homes Value Plot\")\n",
    "    plot.plot()"
   ]
  },
  {
   "source": [
    "## Dataset Splitting"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = boston_df[[\"RM\", \"LSTAT\"]]\n",
    "y = boston_df[\"MEDV\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=TRAIN_SPLIT_RATIO)"
   ]
  },
  {
   "source": [
    "## Linear Regression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The linear model has the RMSE of 5.70 and the R2 score of 0.63.\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.abs(model.predict(X_test))\n",
    "\n",
    "rmse = math.sqrt(mean_squared_error(y_test, y_pred))\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(\"The linear model has the RMSE of {:.2f} and the R2 score of {:.2f}.\".format(rmse, r2))"
   ]
  },
  {
   "source": [
    "## Polynomial Regression"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "The polynomial model with the degree of 2 has the RMSE of 4.95 and the R2 score of 0.72.\nThe polynomial model with the degree of 3 has the RMSE of 4.95 and the R2 score of 0.72.\nThe polynomial model with the degree of 4 has the RMSE of 5.20 and the R2 score of 0.70.\nThe polynomial model with the degree of 5 has the RMSE of 5.07 and the R2 score of 0.71.\n"
     ]
    }
   ],
   "source": [
    "for degree in range(MIN_POLYNOMIAL_DEGREE, MAX_POLYNOMIAL_DEGREE + 1):\n",
    "    transformer = PolynomialFeatures(degree=degree)\n",
    "    X_train_transformed = transformer.fit_transform(X_train)\n",
    "    X_test_transformed = transformer.fit_transform(X_test)\n",
    "\n",
    "    model = LinearRegression()\n",
    "    model.fit(X_train_transformed, y_train)\n",
    "\n",
    "    y_pred = np.abs(model.predict(X_test_transformed))\n",
    "    rmse = math.sqrt(mean_squared_error(y_test, y_pred))\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(\"The polynomial model with the degree of {} has the RMSE of {:.2f} and the R2 score of {:.2f}.\".format(degree, rmse, r2))"
   ]
  }
 ]
}