Classification model to predict whether a person makes over $50k a year

Problem statement :

Create a classification model to predict whether a person makes over $50k a year.

Context :

This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker (Data Mining and Visualization, Silicon Graphics).

Details of features :

The columns are described as follows:

• Age
• Workclass
• Fnlwgt
• Education
• education_num
• marital_status
• occupation
• relationship
• race
• sex
• capital_gain
• capital_loss
• native_country
• income

Dataset :

https://drive.google.com/file/d/1iT33AiIyE2_vg8eMCtIDPJPJfKkjvdQh/view?usp=sharing

Steps to create the model :

• Data Collection : Gather a dataset that includes features such as age, education, occupation, marital status, and capital gain/loss, as well as a label indicating whether the person makes over $50k a year.

• Data Preprocessing : Preprocess the dataset by removing missing values, converting categorical variables to numerical values, and scaling/normalizing the features as needed.

• Model Selection : Choose a classification algorithm to use for the model, such as logistic regression, decision tree, random forest, KNN or support vector machines (SVM).

• Model Training : Split the preprocessed dataset into training and testing sets, and use the training set to train the chosen classification model.

• Model Evaluation : Use the trained model to make predictions on the testing set, and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.

Results :