# Customer Purchase Prediction with Logistic Regression - A Beginner's End-to-End ML Project

> Source: <https://dev.to/waleedshamhan2050sys/customer-purchase-prediction-with-logistic-regression-a-beginners-end-to-end-ml-project-2d24>
> Published: 2026-10-16 16:38:45+00:00

When I finished my first machine learning lectures, I wanted a project that used *everything* I had learned: data loading, preprocessing, train-test split, scaling, training, and evaluation. So I built a small but complete project: **predicting whether a customer will purchase a product**, based on their age, salary, experience, and gender.

The dataset is tiny (8 customers), which makes it perfect for learning — you can follow every number by hand.

| Age | Salary | Experience | Gender | Purchase | 
|---|---|---|---|---|
| 25 | 500 | 2 | Male | 0 | 
| 32 | 800 | 5 | Female | 1 | 
| 45 | 1200 | 15 | Male | 1 | 
| 22 | 400 | 1 | Female | 0 | 
| 38 | 950 | 8 | Male | 1 | 
| 28 | 600 | 3 | Female | 0 | 
| 50 | 1500 | 20 | Male | 1 | 
| 30 | 700 | 4 | Female | 1 | 

`Purchase` is the target: `1` means the customer bought, `0` means they didn't. The rest are the features.

Models work with numbers, so the `Gender` column needs encoding: Male = 0, Female = 1.

``` python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

data = {
    'Age': [25, 32, 45, 22, 38, 28, 50, 30],
    'Salary': [500, 800, 1200, 400, 950, 600, 1500, 700],
    'Experience': [2, 5, 15, 1, 8, 3, 20, 4],
    'Gender': ['Male', 'Female', 'Male', 'Female', 'Male', 'Female', 'Male', 'Female'],
    'Purchase': [0, 1, 1, 0, 1, 0, 1, 1]
}
df = pd.DataFrame(data)
df['Gender'] = df['Gender'].map({'Male': 0, 'Female': 1})
X = df[['Age', 'Salary', 'Experience', 'Gender']]
y = df['Purchase']

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)
# Train shape: (6, 4) — Test shape: (2, 4)
```

`test_size=0.2` means 20% of the data goes to testing. `random_state=42` makes the split reproducible.

Age ranges up to 50, Salary up to 1500. Without scaling, the bigger numbers dominate the model. `StandardScaler` fixes that — and the key rule is: **fit only on the training data**, then transform both sets.

```
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)  # never fit_transform on test data!
```

Since the target is yes/no, this is a **classification** problem, and logistic regression is the natural first model:

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

y_pred = model.predict(X_test)
print("Actual:   ", list(y_test))   # [0, 1]
print("Predicted:", list(y_pred))   # [0, 1]
print("Accuracy: ", accuracy_score(y_test, y_pred))  # 1.0
```

The model predicted both test customers correctly: accuracy **1.0**.

Now the fun part — a brand-new customer: age 36, salary 950, 6 years of experience, female. The new data must go through the **same scaler** (transform, not fit):

```
new_customer = [[36, 950, 6, 1]]
new_customer_scaled = scaler.transform(new_customer)
prediction = model.predict(new_customer_scaled)
print("Approved" if prediction[0] == 1 else "Not Approved")  # Approved
```

Result: **Approved (Yes)** — the model predicts this customer will purchase.

If you are learning ML, build this project yourself and change one thing at a time: try a different `random_state`, add a feature, or compare logistic regression with a decision tree. That is where the real learning happens.