How to Build an Income Prediction Model for a Given Dataset Using Python?
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Condition for Building an Income Prediction Model for a Given Dataset Using Python?
Description:
Income prediction is a common machine learning problem where we predict a person's income level based
on certain features. This project focuses on predicting whether a person earns more or less than
$50K/year based on their characteristics such as age, education, occupation, marital status,
and more. A classification approach is used to determine the income class (greater than $50K or
less than $50K).
In this project, we use a dataset (typically the Adult Income Dataset) for training and evaluation.
The dataset contains multiple attributes such as age, workclass, education, occupation, etc.,
and the task is to predict whether a person earns more than $50K or not based on these attributes.
Step-by-Step Process
Data Loading and Preprocessing:
Load the dataset. Handle missing data, if any. Convert categorical data into numerical features using encoding techniques.
Exploratory Data Analysis (EDA):
Visualize data distributions. Check correlations between features. Plot the heatmap for correlation of features.
Data Splitting:
Split the dataset into training and testing datasets (e.g., 80% for training and 20% for testing).
Model Selection and Training:
Choose a classification model (e.g., Random Forest, Decision Tree, Logistic Regression). Train the model using the training dataset.
Model Evaluation:
Predict the income on the test set. Evaluate the model using accuracy, precision, recall, and F1-score.
Visualization:
Plot ROC curves. Generate confusion matrix and classification metrics.
Output:
Predicted classes (income greater than or less than $50K). Evaluate the classification performance metrics.
Why Should We Choose This Approach?
Random Forest and Decision Trees:
These models are robust for classification problems and perform well on tabular data with mixed numerical and categorical features.
Heatmaps:
Heatmaps help in identifying relationships between variables and can show which features are important in predicting income.
Classification Metrics:
Accuracy, precision, recall, and F1-score are standard in evaluating the performance of machine learning models for classification tasks.
Sample Source Code
# Importing necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
from sklearn.preprocessing import StandardScaler
for col in categorical_columns:
data[col] = encoder.fit_transform(data[col])
# Splitting dataset into features and target variable
X = data.drop('income', axis=1)
y = data['income']
# Split the data into training and testing datasets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Research Breakthrough Possible @S-Logix
