kaggle_exercise.ipynb

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  "metadata": {
    "colab": {
      "name": "kaggle exercise.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/michaelprinc/Python/blob/master/kaggle_exercise.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BmO6lJ6Sn2En",
        "colab_type": "text"
      },
      "source": [
        "First"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZzzu0pWSCR9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# save filepath to variable for easier access\n",
        "melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\n",
        "# read the data and store data in DataFrame titled melbourne_data\n",
        "melbourne_data = pd.read_csv(melbourne_file_path) \n",
        "# print a summary of the data in Melbourne data\n",
        "melbourne_data.describe()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzJJlyRMSvI5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set up code checking\n",
        "from learntools.core import binder\n",
        "binder.bind(globals())\n",
        "from learntools.machine_learning.ex2 import *\n",
        "print(\"Setup Complete\")\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Path of the file to read\n",
        "iowa_file_path = '../input/home-data-for-ml-course/train.csv'\n",
        "\n",
        "# Fill in the line below to read the file into a variable home_data\n",
        "home_data = pd.read_csv(iowa_file_path)\n",
        "\n",
        "# Call line below with no argument to check that you've loaded the data correctly\n",
        "step_1.check()\n",
        "\n",
        "# Print summary statistics in next line\n",
        "home_data.describe()\n",
        "\n",
        "# What is the average lot size (rounded to nearest integer)?\n",
        "avg_lot_size = round(10516.828082,0)\n",
        "\n",
        "# As of today, how old is the newest home (current year - the date in which it was built)\n",
        "newest_home_age = round(2019-2010.000000,0)\n",
        "\n",
        "# Checks your answers\n",
        "step_2.check()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhqSSw39n8HJ",
        "colab_type": "text"
      },
      "source": [
        "Second"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hznu0F6WVrGN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'\n",
        "melbourne_data = pd.read_csv(melbourne_file_path) \n",
        "melbourne_data.columns\n",
        "\n",
        "# The Melbourne data has some missing values (some houses for which some variables weren't recorded.)\n",
        "# We'll learn to handle missing values in a later tutorial.  \n",
        "# Your Iowa data doesn't have missing values in the columns you use. \n",
        "# So we will take the simplest option for now, and drop houses from our data. \n",
        "# Don't worry about this much for now, though the code is:\n",
        "\n",
        "# dropna drops missing values (think of na as \"not available\")\n",
        "melbourne_data = melbourne_data.dropna(axis=0)\n",
        "\n",
        "y = melbourne_data.Price\n",
        "\n",
        "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']\n",
        "\n",
        "X = melbourne_data[melbourne_features]\n",
        "X.describe()\n",
        "X.head()\n",
        "\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "# Define model. Specify a number for random_state to ensure same results each run\n",
        "melbourne_model = DecisionTreeRegressor(random_state=1)\n",
        "\n",
        "# Fit model\n",
        "melbourne_model.fit(X, y)\n",
        "\n",
        "print(\"Making predictions for the following 5 houses:\")\n",
        "print(X.head())\n",
        "print(\"The predictions are\")\n",
        "print(melbourne_model.predict(X.head()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7P8c9Ibn_yE",
        "colab_type": "text"
      },
      "source": [
        "Third"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cjiHAUQXZbzW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code you have previously used to load data\n",
        "import pandas as pd\n",
        "\n",
        "# Path of the file to read\n",
        "iowa_file_path = '../input/home-data-for-ml-course/train.csv'\n",
        "\n",
        "home_data = pd.read_csv(iowa_file_path)\n",
        "\n",
        "# Set up code checking\n",
        "from learntools.core import binder\n",
        "binder.bind(globals())\n",
        "from learntools.machine_learning.ex3 import *\n",
        "\n",
        "print(\"Setup Complete\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cFRzp0idS78",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the list of features below\n",
        "feature_names = ['LotArea'\n",
        "    ,'YearBuilt'\n",
        "    ,'1stFlrSF'\n",
        "    ,'2ndFlrSF'\n",
        "    ,'FullBath'\n",
        "    ,'BedroomAbvGr'\n",
        "    ,'TotRmsAbvGrd']\n",
        "\n",
        "# select data corresponding to features in feature_names\n",
        "X = home_data[feature_names]\n",
        "\n",
        "step_2.check()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-hwwxIud_A9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Review data\n",
        "# print description or statistics from X\n",
        "print(X.describe())\n",
        "\n",
        "# print the top few lines\n",
        "print(X.head())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMgyDeTsfEq1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "#specify the model. \n",
        "#For model reproducibility, set a numeric value for random_state when specifying the model\n",
        "iowa_model = DecisionTreeRegressor(random_state=1)\n",
        "\n",
        "# Fit the model\n",
        "iowa_model.fit(X, y)\n",
        "\n",
        "step_3.check()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kLIMRm_wfRSE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = iowa_model.predict(X)\n",
        "print(predictions)\n",
        "step_4.check()\n",
        "print(y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hce4_ZL3oC87",
        "colab_type": "text"
      },
      "source": [
        "Fourth"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hgmjswhBk2Y-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "predicted_home_prices = melbourne_model.predict(X)\n",
        "mean_absolute_error(y, predicted_home_prices)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9U_zSHvaqYR_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# split data into training and validation data, for both features and target\n",
        "# The split is based on a random number generator. Supplying a numeric value to\n",
        "# the random_state argument guarantees we get the same split every time we\n",
        "# run this script.\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n",
        "# Define model\n",
        "melbourne_model = DecisionTreeRegressor()\n",
        "# Fit model\n",
        "melbourne_model.fit(train_X, train_y)\n",
        "\n",
        "# get predicted prices on validation data\n",
        "val_predictions = melbourne_model.predict(val_X)\n",
        "print(mean_absolute_error(val_y, val_predictions))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oioRzrEwvYWc",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_JZwaoGkvapL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Code you have previously used to load data\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "​\n",
        "# Path of the file to read\n",
        "iowa_file_path = '../input/home-data-for-ml-course/train.csv'\n",
        "​\n",
        "home_data = pd.read_csv(iowa_file_path)\n",
        "y = home_data.SalePrice\n",
        "feature_columns = ['LotArea', 'YearBuilt', '1stFlrSF', '2ndFlrSF', 'FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\n",
        "X = home_data[feature_columns]\n",
        "​\n",
        "# Specify Model\n",
        "iowa_model = DecisionTreeRegressor()\n",
        "# Fit Model\n",
        "iowa_model.fit(X, y)\n",
        "​\n",
        "print(\"First in-sample predictions:\", iowa_model.predict(X.head()))\n",
        "print(\"Actual target values for those homes:\", y.head().tolist())\n",
        "​\n",
        "# Set up code checking\n",
        "from learntools.core import binder\n",
        "binder.bind(globals())\n",
        "from learntools.machine_learning.ex4 import *\n",
        "print(\"Setup Complete\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ-C9gqyvfs1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# Import the train_test_split function and uncomment\n",
        "from sklearn import train_test_split\n",
        "​\n",
        "# fill in and uncomment\n",
        "train_X, val_X, train_y, val_y = train_test_split (random_state=1)\n",
        "​\n",
        "\n",
        "# You imported DecisionTreeRegressor in your last exercise\n",
        "# and that code has been copied to the setup code above. So, no need to\n",
        "# import it again\n",
        "\n",
        "# Specify the model\n",
        "iowa_model = DecisionTreeRegressor(random_state=1)\n",
        "# Fit Model\n",
        "iowa_model.fit(train_X, train_y)\n",
        "\n",
        "# Fit iowa_model with the training data.\n",
        "____\n",
        "step_2.check()\n",
        "\n",
        "val_predictions = iowa_model.predict(val_X)\n",
        "\n",
        "# print the top few validation predictions\n",
        "print(val_predictions[:5])\n",
        "# print the top few actual prices from validation data\n",
        "print(val_y[:5])\n",
        "\n",
        "\n",
        "​"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}