Michael Rey commited on
Commit
759a794
·
1 Parent(s): 07e65eb

initial commit

Browse files
Files changed (3) hide show
  1. Social_Network_Ads.csv +401 -0
  2. app.py +73 -0
  3. requirements.txt +4 -0
Social_Network_Ads.csv ADDED
@@ -0,0 +1,401 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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app.py ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ import numpy as np
4
+ import matplotlib.pyplot as plt
5
+ import seaborn as sns
6
+ from sklearn.model_selection import train_test_split
7
+ from sklearn.preprocessing import StandardScaler
8
+ from sklearn.linear_model import LogisticRegression
9
+ from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
10
+
11
+ # Load dataset
12
+ df = pd.read_csv("Social_Network_Ads.csv")
13
+ df = df.drop(columns=["User ID"]) # Remove User ID
14
+
15
+ # App Title
16
+ st.title("💼 Social Network Ads - Customer Purchase Prediction")
17
+ st.write("### Predict if a user will purchase a product based on Age & Salary.")
18
+
19
+ # Dataset Preview
20
+ st.write("#### Dataset Preview:")
21
+ st.dataframe(df.head())
22
+
23
+ # Data Distribution
24
+ st.write("#### Data Distribution")
25
+ fig, ax = plt.subplots(1, 2, figsize=(12, 5))
26
+ sns.histplot(df["Age"], bins=20, kde=True, ax=ax[0], color="blue")
27
+ ax[0].set_title("Age Distribution")
28
+ sns.histplot(df["EstimatedSalary"], bins=20, kde=True, ax=ax[1], color="green")
29
+ ax[1].set_title("Salary Distribution")
30
+ st.pyplot(fig)
31
+
32
+ # Preprocessing
33
+ X = df[["Age", "EstimatedSalary"]]
34
+ y = df["Purchased"]
35
+ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
36
+ scaler = StandardScaler()
37
+ X_train = scaler.fit_transform(X_train)
38
+ X_test = scaler.transform(X_test)
39
+
40
+ # Train Logistic Regression Model
41
+ model = LogisticRegression()
42
+ model.fit(X_train, y_train)
43
+ y_pred = model.predict(X_test)
44
+ accuracy = accuracy_score(y_test, y_pred)
45
+ conf_matrix = confusion_matrix(y_test, y_pred)
46
+
47
+ # Model Performance
48
+ st.write("### 📊 Model Performance")
49
+ st.write(f"**Model Accuracy:** {accuracy:.2f}")
50
+ st.write("#### Classification Report:")
51
+ st.text(classification_report(y_test, y_pred))
52
+
53
+ st.write("#### Confusion Matrix:")
54
+ fig, ax = plt.subplots()
55
+ sns.heatmap(conf_matrix, annot=True, fmt="d", cmap="Blues", xticklabels=["Not Purchased", "Purchased"], yticklabels=["Not Purchased", "Purchased"])
56
+ st.pyplot(fig)
57
+
58
+ # Prediction
59
+ st.write("### 🤖 Try the Model")
60
+ st.write("Enter details to check if a customer will purchase.")
61
+
62
+ age = st.slider("Select Age", min_value=int(X["Age"].min()), max_value=int(X["Age"].max()), value=30)
63
+ salary = st.slider("Select Estimated Salary", min_value=int(X["EstimatedSalary"].min()), max_value=int(X["EstimatedSalary"].max()), value=50000)
64
+
65
+ if st.button("Predict Purchase"):
66
+ input_data = scaler.transform([[age, salary]])
67
+ prediction = model.predict(input_data)[0]
68
+ prediction_proba = model.predict_proba(input_data)[0]
69
+
70
+ st.subheader("Prediction Result")
71
+ result_text = "Yes! The user is likely to purchase." if prediction == 1 else "No, the user is not likely to purchase."
72
+ st.success(result_text) if prediction == 1 else st.warning(result_text)
73
+ st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
requirements.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ streamlit
2
+ pandas
3
+ numpy
4
+ scikit-learn