Mini Machine Learning Project (End-to-End)

Congratulations. If you are reading this lesson, you have already learned the complete foundation of Machine Learning.

Now we bring everything together and build a real, end-to-end Machine Learning project using the Dataplexa ML Dataset.

Project Objective

The goal of this project is to predict whether a customer will make a purchase based on their demographic and behavioral data.

This is a very common real-world business problem used in marketing, fintech, e-commerce, and customer analytics.

Step 1: Load the Dataset

We start by loading the Dataplexa ML dataset that you downloaded earlier.

import pandas as pd

data = pd.read_csv("dataplexa_ml_dataset.csv")
data.head()

At this stage, we verify that the dataset has loaded correctly and inspect the columns.

Step 2: Understand the Data

Each row represents a customer.

The dataset contains information such as age, income, education level, spending score, and past interactions.

The target variable indicates whether the customer made a purchase.

Step 3: Data Preparation

Before training any model, we must clean and prepare the data.

This includes handling missing values, encoding categorical variables, and scaling numerical features.

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X = data.drop("Purchased", axis=1)
y = data["Purchased"]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Now the data is ready for modeling.

Step 4: Train the Model

We use Logistic Regression as our first predictive model.

This model is interpretable and effective for binary classification problems.

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X_train, y_train)

Step 5: Model Evaluation

After training, we evaluate the model on unseen test data.

from sklearn.metrics import accuracy_score, classification_report

y_pred = model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

print(classification_report(y_test, y_pred))

This tells us how well the model performs and whether it generalizes properly.

Step 6: Business Interpretation

Machine Learning is valuable only when it supports decisions.

Using this model, a company can identify customers with high purchase probability and focus marketing efforts on them.

This reduces cost and increases conversion.

Final Output

You have successfully built:

• A real dataset-driven ML project • A complete preprocessing pipeline • A trained and evaluated model • A business-ready prediction system

Exercises

Quick Quiz

Final Words

Completing this course is an important milestone. What truly matters next is applying these concepts in real projects.

Keep building, keep experimenting, and keep strengthening your skills. This is how strong professionals are made.