{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![MLU Logo](../../data/MLU_Logo.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning University - Decision Trees and Ensemble Models\n", "\n", "\n", "## Final Project \n", "\n", "Here is the breakdown of this notebook:\n", "\n", "1. Read the dataset (Given) \n", "2. Train a model (Implement)\n", " * Exploratory Data Analysis\n", " * Select features to build the model\n", " * Data processing\n", " * Model training\n", "3. Make predictions on the test dataset (Implement)\n", "\n", "__Austin Animal Center Dataset__:\n", "\n", "In this notebook, we are working with pet adoption data from __Austin Animal Center__. We joined two datasets that cover intake and outcome of animals. Intake data is available from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Intakes/wter-evkm) and outcome is from [here](https://data.austintexas.gov/Health-and-Community-Services/Austin-Animal-Center-Outcomes/9t4d-g238). We want you to __predict whether a pet is adopted within the 30 days stay time in the animal center.__ \n", "\n", "__Dataset schema:__ \n", "- __Pet ID__ - Unique ID of pet\n", "- __Outcome Type__ - State of pet at the time of recording the outcome\n", "- __Sex upon Outcome__ - Sex of pet at outcome\n", "- __Name__ - Name of pet \n", "- __Found Location__ - Found location of pet before entered the center\n", "- __Intake Type__ - Circumstances bringing the pet to the center\n", "- __Intake Condition__ - Health condition of pet when entered the center\n", "- __Pet Type__ - Type of pet\n", "- __Sex upon Intake__ - Sex of pet when entered the center\n", "- __Breed__ - Breed of pet \n", "- __Color__ - Color of pet \n", "- __Age upon Intake Days__ - Age of pet when entered the center (days)\n", "- __Time at Center__ - Time at center (0 = less than 30 days; 1 = more than 30 days). This is the value to predict. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install -q -r ../../requirements.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Read the datasets (Given)\n", "(Go to top)\n", "\n", "Let's read the datasets into dataframes, using Pandas." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The shape of the training dataset is: (71538, 13)\n", "The shape of the test dataset is: (23846, 12)\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", " \n", "training_data = pd.read_csv('../../data/final_project/training.csv')\n", "test_data = pd.read_csv('../../data/final_project/test_features.csv')\n", "\n", "print('The shape of the training dataset is:', training_data.shape)\n", "print('The shape of the test dataset is:', test_data.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Train a model (Implement)\n", "(Go to top)\n", "\n", " * Exploratory Data Analysis\n", " * Select features to build the model\n", " * Data processing\n", " * Model training\n", "\n", "### 2.1 Exploratory Data Analysis \n", "(Go to Train a model)\n", "\n", "We look at number of rows, columns and some simple statistics of the dataset." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Pet IDOutcome TypeSex upon OutcomeNameFound LocationIntake TypeIntake ConditionPet TypeSex upon IntakeBreedColorAge upon Intake DaysTime at Center
0A745079TransferUnknownNaN7920 Old Lockhart in Travis (TX)StrayNormalCatUnknownDomestic Shorthair MixBlue30
1A801765TransferIntact FemaleNaN5006 Table Top in Austin (TX)StrayNormalCatIntact FemaleDomestic ShorthairBrown Tabby/White280
2A667965TransferNeutered MaleNaN14100 Thermal Dr in Austin (TX)StrayNormalDogNeutered MaleChihuahua Shorthair MixBrown/Tan18250
3A687551TransferIntact MaleNaN5811 Cedardale Dr in Austin (TX)StrayNormalCatIntact MaleDomestic Shorthair MixBrown Tabby280
4A773004AdoptionNeutered Male*BorisHighway 290 And Arterial A in Austin (TX)StrayNormalDogIntact MaleChihuahua Shorthair MixTricolor/Cream3650
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" ], "text/plain": [ " Pet ID Outcome Type Sex upon Outcome Name \\\n", "0 A745079 Transfer Unknown NaN \n", "1 A801765 Transfer Intact Female NaN \n", "2 A667965 Transfer Neutered Male NaN \n", "3 A687551 Transfer Intact Male NaN \n", "4 A773004 Adoption Neutered Male *Boris \n", "\n", " Found Location Intake Type Intake Condition \\\n", "0 7920 Old Lockhart in Travis (TX) Stray Normal \n", "1 5006 Table Top in Austin (TX) Stray Normal \n", "2 14100 Thermal Dr in Austin (TX) Stray Normal \n", "3 5811 Cedardale Dr in Austin (TX) Stray Normal \n", "4 Highway 290 And Arterial A in Austin (TX) Stray Normal \n", "\n", " Pet Type Sex upon Intake Breed Color \\\n", "0 Cat Unknown Domestic Shorthair Mix Blue \n", "1 Cat Intact Female Domestic Shorthair Brown Tabby/White \n", "2 Dog Neutered Male Chihuahua Shorthair Mix Brown/Tan \n", "3 Cat Intact Male Domestic Shorthair Mix Brown Tabby \n", "4 Dog Intact Male Chihuahua Shorthair Mix Tricolor/Cream \n", "\n", " Age upon Intake Days Time at Center \n", "0 3 0 \n", "1 28 0 \n", "2 1825 0 \n", "3 28 0 \n", "4 365 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Implement here\n", "\n", "training_data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Pet IDOutcome TypeSex upon OutcomeNameFound LocationIntake TypeIntake ConditionPet TypeSex upon IntakeBreedColorAge upon Intake Days
0A782657AdoptionSpayed FemaleNaN1911 Dear Run Drive in Austin (TX)StrayNormalDogIntact FemaleLabrador Retriever MixBlack60
1A804622AdoptionNeutered MaleNaN702 Grand Canyon in Austin (TX)StrayNormalDogIntact MaleBoxer/Anatol ShepherdBrown/Tricolor60
2A786693Return to OwnerNeutered MaleZeusAustin (TX)Public AssistNormalDogNeutered MaleAustralian Cattle Dog/Pit BullBlack/White3285
3A693330AdoptionSpayed FemaleHopeLevander Loop & Airport Blvd in Austin (TX)StrayNormalDogIntact FemaleMiniature PoodleGray1825
4A812431AdoptionNeutered MaleNaNAustin (TX)Owner SurrenderInjuredCatIntact MaleDomestic ShorthairBlue/White210
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" ], "text/plain": [ " Pet ID Outcome Type Sex upon Outcome Name \\\n", "0 A782657 Adoption Spayed Female NaN \n", "1 A804622 Adoption Neutered Male NaN \n", "2 A786693 Return to Owner Neutered Male Zeus \n", "3 A693330 Adoption Spayed Female Hope \n", "4 A812431 Adoption Neutered Male NaN \n", "\n", " Found Location Intake Type \\\n", "0 1911 Dear Run Drive in Austin (TX) Stray \n", "1 702 Grand Canyon in Austin (TX) Stray \n", "2 Austin (TX) Public Assist \n", "3 Levander Loop & Airport Blvd in Austin (TX) Stray \n", "4 Austin (TX) Owner Surrender \n", "\n", " Intake Condition Pet Type Sex upon Intake Breed \\\n", "0 Normal Dog Intact Female Labrador Retriever Mix \n", "1 Normal Dog Intact Male Boxer/Anatol Shepherd \n", "2 Normal Dog Neutered Male Australian Cattle Dog/Pit Bull \n", "3 Normal Dog Intact Female Miniature Poodle \n", "4 Injured Cat Intact Male Domestic Shorthair \n", "\n", " Color Age upon Intake Days \n", "0 Black 60 \n", "1 Brown/Tricolor 60 \n", "2 Black/White 3285 \n", "3 Gray 1825 \n", "4 Blue/White 210 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Implement here\n", "\n", "test_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2 Select features to build the model \n", "(Go to Train a model)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Implement here\n", "\n", "# numerical_features = ...\n", "# categorical_features = ...\n", "# text_features = ..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Data Processing \n", "(Go to Train a model)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Implement here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.4 Model training \n", "(Go to Train a model)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Implement here\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Make predictions on the test dataset (Implement)\n", "(Go to top)\n", "\n", "Use the test set to make predictions with the trained model." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Implement here\n", "\n", "# test_predictions = ..." ] } ], "metadata": { "kernelspec": { "display_name": "conda_mxnet_p36", "language": "python", "name": "conda_mxnet_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 2 }