Logositc&LinearRegression.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Logositc&LinearRegression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aayushktyagi/DeepLearning_Resources/blob/master/Logositc%26LinearRegression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCZFquVgKZzo",
        "colab_type": "text"
      },
      "source": [
        "Content\n",
        "\n",
        "1.   Logistic regression using Heart Disease Prediction dataset\n",
        "2.   Linear regression using Fuel proce prediction dataset\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4gxD1wKKNwM",
        "colab_type": "code",
        "outputId": "3c93a174-3e58-48ff-bd73-1f1510d8c4ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t8J8wJsgK27C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip3 install tensorflow==2.0.0-beta1\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import csv\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers \n",
        "import seaborn as sns \n",
        "from tensorflow import feature_column\n",
        "from tensorflow.keras import layers\n",
        "from sklearn.model_selection import train_test_split\n",
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oz-JHFw5LEKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load dataset \n",
        "URL = 'https://storage.googleapis.com/applied-dl/heart.csv'\n",
        "dataframe = pd.read_csv(URL)\n",
        "dataframe.head()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxByV8F1cQD_",
        "colab_type": "code",
        "outputId": "ded4c8ca-323b-414d-eaa5-b252180cc4ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "train, test = train_test_split(dataframe, test_size=0.2)\n",
        "train, val = train_test_split(train, test_size=0.2)\n",
        "print(len(train), 'train examples')\n",
        "print(len(val), 'validation examples')\n",
        "print(len(test), 'test examples')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "193 train examples\n",
            "49 validation examples\n",
            "61 test examples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiu75uiZdljR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# A utility method to create a tf.data dataset from a Pandas Dataframe\n",
        "def df_to_dataset(dataframe, shuffle=True, batch_size=32):\n",
        "  dataframe = dataframe.copy()\n",
        "  labels = dataframe.pop('target')\n",
        "  ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))\n",
        "  if shuffle:\n",
        "    ds = ds.shuffle(buffer_size=len(dataframe))\n",
        "  ds = ds.batch(batch_size)\n",
        "  return ds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W44wrXhHds1R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_columns = []\n",
        "\n",
        "# numeric cols\n",
        "for header in ['age', 'trestbps', 'chol', 'thalach', 'oldpeak', 'slope', 'ca']:\n",
        "  feature_columns.append(feature_column.numeric_column(header))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4lCSzoidwDJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature_layer = tf.keras.layers.DenseFeatures(feature_columns)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZnRI5b2eAM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "train_ds = df_to_dataset(train, batch_size=batch_size)\n",
        "val_ds = df_to_dataset(val, shuffle=False, batch_size=batch_size)\n",
        "test_ds = df_to_dataset(test, shuffle=False, batch_size=batch_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzOfZBo3eLlF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  feature_layer,\n",
        "  layers.Dense(128, activation='relu'),\n",
        "  layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(train_ds,\n",
        "          validation_data=val_ds,\n",
        "          epochs=10)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yucMrBvTeZNi",
        "colab_type": "code",
        "outputId": "8fc85293-13d0-4846-b315-33be5de62a08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "loss, accuracy = model.evaluate(test_ds)\n",
        "print(\"Accuracy\", accuracy)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 0s 18ms/step - loss: 1.0056 - accuracy: 0.6557\n",
            "Accuracy 0.6557377\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_VnYs3ea5nn6",
        "colab_type": "text"
      },
      "source": [
        "Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LXdaMwqh5m63",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import linear_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZAKgG6G_iV9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 825
        },
        "outputId": "d0662d3e-2b84-4913-d5ef-e97f68bd279c"
      },
      "source": [
        "diabetes = pd.read_csv(\"drive/My Drive/Dataset/diabetes.csv\")\n",
        "print(diabetes.head())\n",
        "print(diabetes.info())\n",
        "print(\"Diabetes data set dimensions : {}\".format(diabetes.shape))\n",
        "corr = diabetes.corr()\n",
        "sns.heatmap(corr, xticklabels=corr.columns,yticklabels=corr.columns)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Pregnancies  Glucose  BloodPressure  ...  DiabetesPedigreeFunction  Age  Outcome\n",
            "0            6      148             72  ...                     0.627   50        1\n",
            "1            1       85             66  ...                     0.351   31        0\n",
            "2            8      183             64  ...                     0.672   32        1\n",
            "3            1       89             66  ...                     0.167   21        0\n",
            "4            0      137             40  ...                     2.288   33        1\n",
            "\n",
            "[5 rows x 9 columns]\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            "Pregnancies                 768 non-null int64\n",
            "Glucose                     768 non-null int64\n",
            "BloodPressure               768 non-null int64\n",
            "SkinThickness               768 non-null int64\n",
            "Insulin                     768 non-null int64\n",
            "BMI                         768 non-null float64\n",
            "DiabetesPedigreeFunction    768 non-null float64\n",
            "Age                         768 non-null int64\n",
            "Outcome                     768 non-null int64\n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n",
            "Diabetes data set dimensions : (768, 9)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5692a190b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
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wGsfe0861rknXeFLSwvR+pqTt2rjmnq21/af9IyTN7dqnMzNrXLE4OvySNEnStKrXpC6E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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vauwGs1N2fW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Split dataset \n",
        "dfTrain = diabetes[:650]\n",
        "dfVal = diabetes[650:750]\n",
        "dfTest = diabetes[750:]\n",
        "\n",
        "#Split labels\n",
        "trainLabel = np.asarray(dfTrain['Outcome'])\n",
        "trainData = np.asarray(dfTrain.drop('Outcome',1))\n",
        "valLabel = np.asarray(dfVal['Outcome'])\n",
        "valdata = np.asarray(dfVal.drop('Outcome',1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH_6QdCEQhIT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "797525bf-5085-467e-8550-b3f967fee79c"
      },
      "source": [
        "# Fit model\n",
        "linear_reg = linear_model.LinearRegression()\n",
        "linear_reg.fit(trainData , trainLabel)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pN_f4AxQQefj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "15266668-e8ae-4fed-919d-14d0fc36c571"
      },
      "source": [
        "plt.scatter(valLabel,linear_reg.predict(valdata))\n",
        "plt.xlabel('old data')\n",
        "plt.ylabel('predicted data')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'predicted data')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lyxpwd1tEnC9pB8mEgGkRcXIuGZmZWabFvT2MVigKi3t7cmkv64rifcnXP86lZTMzm5V1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\nzCyTC4WZmWVyoTAzs0wuFGZmlun/A9v/+CYN8RX8AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}