Predicting Consumer Purchases using Machine Learning

In this project, Our focus is on understanding the underlying factors that influence a consumer's choice to make a purchase, and we explore the predictive accuracy of three prominent machine learning models: Decision Tree, Linear Regression, and Logistic Regression. we delve into the realm of predictive analytics by harnessing the power of machine learning algorithms to forecast consumer purchasing decisions.

Our dataset comprises valuable information about users, including their Gender, Age, Estimated Salary, and a Purchased label (0 or 1) indicating whether they made a purchase. By extracting insights from this data, we aim to build robust predictive models that can distinguish potential buyers from non-buyers based on their demographic attributes and estimated income.

The project follows a systematic approach:

Data Preprocessing: We begin by thoroughly examining and cleaning the dataset to ensure its quality and reliability. This step involves handling missing values, encoding categorical variables, and normalizing numerical features.

Exploratory Data Analysis: We delve into the dataset to uncover patterns and correlations between variables. Visualization techniques help us gain insights into the relationships between Age, Estimated Salary, and Gender with the likelihood of making a purchase.

Feature Selection: We identify the most influential features by employing techniques such as information gain or correlation analysis. This ensures that our models are trained on the most relevant attributes.

Model Building:

a. Decision Tree: We construct a decision tree model that maps out decision rules based on feature splits. This model helps us visualize the decision-making process leading to purchase predictions.

b. Linear Regression: By employing linear regression, we establish a relationship between the predictor variables and the likelihood of making a purchase. This approach allows us to quantify the impact of each attribute.

c. Logistic Regression: With logistic regression, we model the probability of purchase as a function of Age, Estimated Salary, and Gender. This enables us to classify users into distinct categories based on their likelihood to buy.

Model Evaluation: We rigorously assess the performance of each model using appropriate metrics like accuracy, precision, recall, and F1-score. The evaluation enables us to compare the effectiveness of the three models in predicting purchase behavior.

Conclusion and Insights: Based on our analyses and comparisons, we draw conclusions about the effectiveness of the models in predicting purchases. We discuss the strengths and limitations of each model and provide insights into the factors that significantly influence purchase decisions.

Through this project, we aim to provide valuable insights into consumer behavior and contribute to the broader understanding of how machine learning techniques can be harnessed for predictive analytics in marketing and business contexts.