Simple Linear and Logistic Regression.ipynb

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   "name": "python"
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 "cells": [
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   "cell_type": "markdown",
   "source": [
    "# Simple Linear and Logistic Regression\n"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%% md\n"
    }
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   "cell_type": "markdown",
   "source": [
    "# The Technique (Linear Regression)\n",
    "\n",
    "Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task. Regression models a target prediction value based on independent variables. It is mostly used for finding out the relationship between variables and forecasting. Different regression models differ based on – the kind of relationship between dependent and independent variables they are considering, and the number of independent variables getting used."
   ],
   "metadata": {
    "id": "HAHDODSPmg7r",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# The Problem\n",
    "Linear regression is a prediction when a variable (y) is dependent on a second variable (x) based on the regression equation of a given set of data."
   ],
   "metadata": {
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  },
  {
   "cell_type": "markdown",
   "source": [
    "# Simple Linear Regression"
   ],
   "metadata": {
    "id": "4q2QSoElmmZH",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Code"
   ],
   "metadata": {
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    "pycharm": {
     "name": "#%% md\n"
    }
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   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import r2_score\n",
    "# from google.colab import files\n",
    "# upload = files.upload()\n",
    "\n",
    "# Import our WeatherData and store it in the variable weather_data\n",
    "weather_data = pd.read_csv(\"WeatherData.csv\")\n",
    "# Display the data in the notebook\n",
    "print(weather_data.head())\n",
    "\n",
    "# Set x to our input Temperature\n",
    "x = weather_data['Temperature(C)']\n",
    "# Set y to our output humidity, this is the value we trying to predict\n",
    "y = weather_data['Humidity']\n",
    "\n",
    "# Plot Humidity against Temperature\n",
    "plt.scatter(x, y)\n",
    "# Add labels to the graph\n",
    "plt.xlabel('Temperature(C)')\n",
    "plt.ylabel('Humidity')\n",
    "\n",
    "# plt.show()\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.60, random_state=6)\n",
    "x_train = x_train.values.reshape(-1, 1)\n",
    "y_train = y_train.values.reshape(-1, 1)\n",
    "x_test = x_test.values.reshape(-1, 1)\n",
    "y_test = y_test.values.reshape(-1, 1)\n",
    "my_model = LinearRegression()\n",
    "my_model.fit(x_train, y_train)\n",
    "y_pred = my_model.predict(x_test)\n",
    "\n",
    "plt.scatter(x_test, y_test)\n",
    "plt.xlabel('Temperature(C)')\n",
    "plt.ylabel('Humidity')\n",
    "\n",
    "plt.plot(x_test, y_pred)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 383
    },
    "id": "jLTfRX2uo2CU",
    "outputId": "0278e5c4-f31d-4ab0-9c47-2a3347defd4a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "   Temperature(C)  Humidity\n",
      "0        9.472222      0.89\n",
      "1        9.355556      0.86\n",
      "2        9.377778      0.89\n",
      "3        8.288889      0.83\n",
      "4        8.755556      0.83\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Result"
   ],
   "metadata": {
    "id": "lnhu7lSapiiD",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "beta_0 = my_model.intercept_\n",
    "beta_1 = my_model.coef_\n",
    "print('Intercept', beta_0)\n",
    "print('Coefficient', beta_1)\n",
    "print('Mean Square Error:', mean_squared_error(x_test, y_pred))\n",
    "print('Mean Absolute Error', mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Log. Error', mean_squared_log_error(y_test, y_pred))\n",
    "print('Mean abs. per. error', mean_absolute_percentage_error(y_test, y_pred))\n",
    "print('R^2 score', r2_score(y_test, y_pred))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UTdY7bd0uusq",
    "outputId": "bf2733b0-7d83-44f8-8520-4fd90dd4ad3e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Intercept [1.18157531]\n",
      "Coefficient [[-0.03746804]]\n",
      "Mean Square Error: 171.5274349336167\n",
      "Mean Absolute Error 0.047218748892409804\n",
      "Mean Log. Error 0.0012376435851646289\n",
      "Mean abs. per. error 0.07138424977903816\n",
      "R^2 score 0.8756202829785189\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Multiple Linear Regression"
   ],
   "metadata": {
    "id": "VmgbhSw1vIcs",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Code"
   ],
   "metadata": {
    "id": "oYMqG-lfvVq0",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Import Pandas Library, used for data manipulation\n",
    "# Import matplotlib, used to plot our data\n",
    "# Import nump for mathemtical operations\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# from google.colab import files\n",
    "# upload = files.upload()\n",
    "\n",
    "# Import our WeatherDataM and store it in the variable weather_data_m\n",
    "weather_data_m = pd.read_csv('weatherHistory.csv')\n",
    "# Display the data in the notebook\n",
    "print(weather_data_m.head())\n",
    "print(weather_data_m.columns)\n",
    "\n",
    "# Set the features of our model, these are our potential inputs\n",
    "weather_features = ['Temperature (C)', 'Wind Speed (km/h)', 'Pressure (millibars)']\n",
    "\n",
    "# Set the variable X to be all our input columns: Temperature, Wind Speed and Pressure\n",
    "X = weather_data_m[weather_features]\n",
    "\n",
    "# set y to be our output column: Humidity\n",
    "y = weather_data_m.Humidity\n",
    "\n",
    "# plt.subplot enables us to plot mutliple graphs\n",
    "# we produce scatter plots for Humidity against each of our input variables\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.scatter(X['Temperature (C)'], y)\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.scatter(X['Wind Speed (km/h)'], y)\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.scatter(X['Pressure (millibars)'], y)\n",
    "\n",
    "mlr_model = LinearRegression()\n",
    "mlr_model.fit(X, y)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 838
    },
    "id": "IsMbG6pQvXpe",
    "outputId": "3cafad1b-c83d-4dbe-c246-4ef39f17e95a",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                  Formatted Date        Summary Precip Type  Temperature (C)  \\\n",
      "0  2006-04-01 00:00:00.000 +0200  Partly Cloudy        rain         9.472222   \n",
      "1  2006-04-01 01:00:00.000 +0200  Partly Cloudy        rain         9.355556   \n",
      "2  2006-04-01 02:00:00.000 +0200  Mostly Cloudy        rain         9.377778   \n",
      "3  2006-04-01 03:00:00.000 +0200  Partly Cloudy        rain         8.288889   \n",
      "4  2006-04-01 04:00:00.000 +0200  Mostly Cloudy        rain         8.755556   \n",
      "\n",
      "   Apparent Temperature (C)  Humidity  Wind Speed (km/h)  \\\n",
      "0                  7.388889      0.89            14.1197   \n",
      "1                  7.227778      0.86            14.2646   \n",
      "2                  9.377778      0.89             3.9284   \n",
      "3                  5.944444      0.83            14.1036   \n",
      "4                  6.977778      0.83            11.0446   \n",
      "\n",
      "   Wind Bearing (degrees)  Visibility (km)  Loud Cover  Pressure (millibars)  \\\n",
      "0                   251.0          15.8263         0.0               1015.13   \n",
      "1                   259.0          15.8263         0.0               1015.63   \n",
      "2                   204.0          14.9569         0.0               1015.94   \n",
      "3                   269.0          15.8263         0.0               1016.41   \n",
      "4                   259.0          15.8263         0.0               1016.51   \n",
      "\n",
      "                       Daily Summary  \n",
      "0  Partly cloudy throughout the day.  \n",
      "1  Partly cloudy throughout the day.  \n",
      "2  Partly cloudy throughout the day.  \n",
      "3  Partly cloudy throughout the day.  \n",
      "4  Partly cloudy throughout the day.  \n",
      "Index(['Formatted Date', 'Summary', 'Precip Type', 'Temperature (C)',\n",
      "       'Apparent Temperature (C)', 'Humidity', 'Wind Speed (km/h)',\n",
      "       'Wind Bearing (degrees)', 'Visibility (km)', 'Loud Cover',\n",
      "       'Pressure (millibars)', 'Daily Summary'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "metadata": {},
     "execution_count": 91
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Result"
   ],
   "metadata": {
    "id": "d_yxuYkOxIpT",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Import library to produce a 3D plot\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "x1 = X[\"Temperature (C)\"]\n",
    "x2 = X[\"Wind Speed (km/h)\"]\n",
    "\n",
    "ax.scatter(x1, x2, y, c='r', marker='o')\n",
    "\n",
    "# Set axis labels\n",
    "ax.set_xlabel('Temperature (C)')\n",
    "ax.set_ylabel('Wind Speed (km/h)')\n",
    "ax.set_zlabel('Humidity')\n",
    "\n",
    "theta0 = mlr_model.intercept_\n",
    "theta1, theta2, theta3 = mlr_model.coef_\n",
    "theta0, theta1, theta2, theta3\n",
    "\n",
    "y_pred = mlr_model.predict([[15, 21, 17], [14, 20, 15]])\n",
    "print('Predictions: ', y_pred)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "VNpjnZs6xMHN",
    "outputId": "f1479a5d-8ed5-4267-e06e-af129c154e53",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Predictions:  [0.64653655 0.66567866]\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Lab Assigned\n",
    "\n",
    "Apply linear regression on the following example: \n",
    "birth = {\n",
    "    'estriol': ['1', '2', '3', '4', '5'],\n",
    "    'birth_weight': ['1', '1', '2', '2', '4']\n",
    "}"
   ],
   "metadata": {
    "id": "iflYLvnKzh5U",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Code"
   ],
   "metadata": {
    "id": "oi7bjZwRz3Sh",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_log_error\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "birth = {\n",
    "    'estriol': ['1', '2', '3', '4', '5'],\n",
    "    'birth_weight': ['1', '1', '2', '2', '4']\n",
    "}\n",
    "\n",
    "birth_df = pd.DataFrame(birth)  # convert dictionary into dataframe\n",
    "print(birth_df)\n",
    "\n",
    "\n",
    "x = birth_df['birth_weight']\n",
    "# Set y to our output estriol, this is the value we trying to predict\n",
    "y = birth_df['estriol']\n",
    "\n",
    "# Plot estriol against birth weight\n",
    "plt.scatter(x, y)\n",
    "# Add labels to the graph\n",
    "plt.xlabel('birth_weight')\n",
    "plt.ylabel('estriol')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=6)\n",
    "x_train = x_train.values.reshape(-1, 1)\n",
    "y_train = y_train.values.reshape(-1, 1)\n",
    "x_test = x_test.values.reshape(-1, 1)\n",
    "y_test = y_test.values.reshape(-1, 1)\n",
    "my_model = LinearRegression()\n",
    "my_model.fit(x_train, y_train)\n",
    "y_pred = my_model.predict(x_test)\n",
    "\n",
    "# plt.scatter(x_test, y_test)\n",
    "plt.xlabel('birth_weight')\n",
    "plt.ylabel('estriol')\n",
    "\n",
    "# plt.plot(x_test, y_pred)\n",
    "# plt.tight_layout()\n",
    "\n",
    "beta_0 = my_model.intercept_\n",
    "beta_1 = my_model.coef_"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 651
    },
    "id": "HjiAXNaEz5LC",
    "outputId": "68469137-f141-4efb-a80b-74340dc6865e",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "  estriol birth_weight\n",
      "0       1            1\n",
      "1       2            1\n",
      "2       3            2\n",
      "3       4            2\n",
      "4       5            4\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Result"
   ],
   "metadata": {
    "id": "yutQy9twz_HL",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "print('Intercept', beta_0)\n",
    "print('Coefficient', beta_1)\n",
    "print(x_test.shape)\n",
    "print(y_pred.shape)\n",
    "print('Mean Square Error:', mean_squared_error(x_test, y_pred))\n",
    "print('Mean Absolute Error', mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Log. Error', mean_squared_log_error(y_test, y_pred))\n",
    "print('Mean abs. per. error', mean_absolute_percentage_error(y_test, y_pred))\n",
    "print('R^2 score', r2_score(y_test, y_pred))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mpz1jcFw0Blx",
    "outputId": "8e1e0e51-ca03-4642-a520-76efea57c436",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Intercept [0.41666667]\n",
      "Coefficient [[1.16666667]]\n",
      "(1, 1)\n",
      "(1, 1)\n",
      "Mean Square Error: 0.5625\n",
      "Mean Absolute Error 1.25\n",
      "Mean Log. Error 0.08276097481015166\n",
      "Mean abs. per. error 0.3125\n",
      "R^2 score nan\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_regression.py:796: UndefinedMetricWarning: R^2 score is not well-defined with less than two samples.\n",
      "  warnings.warn(msg, UndefinedMetricWarning)\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Conclusion\n",
    "\n",
    "Performing a linear regression makes sense only if the relationship is linear. Other methods must be used to study nonlinear relationships."
   ],
   "metadata": {
    "id": "8XZaDDsY0lFW",
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  }
 ]
}