This project aims to build various Decision Tree Classifier Models to predict the income group of people along with seven demographic variables: Age, Capital Gain/Loss, Education, Hours per Week, Marriage Status and Relationship, Occupation, and Race and Sex. Out of all decision trees, the best model is chosen, refined, and visualized. Subsequently, the model is automated and predictions are made for a new record.
- Data Cleaning and Exploration
- Building Decision Tree Classifier Models
- Evaluating Tree Performance
- Automation
- New Prediction
Python. Python is an interpreted, high-level and general-purpose programming language.
Google Colab. Google colab is a free online Integrated Data Environment.
Install the following packages in Python prior to running the code.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_auc_score
from sklearn.metrics import roc_curve
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from google.colab import drive
drive.mount('/content/drive')After importing drive.mount('/content/drive'), follow instructions in the output to authorize access to Google Drive in order to obtain directories.
Download the data file provided and decompress it. Using Google Drive, create the following folder structure and upload the data here:
/content/drive/MyDrive/MSBA_Colab_2020/ML_Algorithms/CA02/Data
where /content/drive/MyDrive is the standard file path.
This project is licensed under the MIT License.
The project template and dataset were provided by Arin Brahma at Loyola Marymount University.