| 📄 README.md — Exploratory Data Analysis Project | |
| #️⃣ 1. Dataset Overview | |
| This project analyzes an e-commerce customer behavior dataset. | |
| The goal of the EDA was to explore user behavior, identify patterns related to purchases, detect anomalies, and understand factors that influence total spending. | |
| The insights support a future classification task (e.g., predicting customer type or purchase likelihood). | |
| #️⃣ 2. Data Cleaning | |
| Cleaning Steps Performed | |
| The following steps ensured a clean and consistent dataset: | |
| Missing Values | |
| Checked using df.isnull().sum() – no missing values found. | |
| Duplicate Rows | |
| Checked using df.duplicated().sum() – no duplicates detected. | |
| Parsing Dates | |
| Converted the Date column into a proper datetime format. | |
| Normalization & Scaling Checks | |
| Reviewed ranges using df.describe() – no scaling issues. | |
| Invalid Values | |
| Checked Age, Session_Duration_Minutes, Delivery_Time_Days for negative or unrealistic values – all valid. | |
| Final Dataset | |
| After cleaning, the dataset contained no missing values, no duplicates, and all numeric values were within valid ranges. | |
| Code Used | |
| import pandas as pd | |
| df = pd.read_csv("ecommerce_customer_behavior_dataset.csv") | |
| df.isnull().sum() | |
| df.duplicated().sum() | |
| df['Date'] = pd.to_datetime(df['Date'], errors='coerce') | |
| df.describe() | |
| df['Age'].describe() | |
| df['Session_Duration_Minutes'].describe() | |
| df['Delivery_Time_Days'].describe() | |
| df = df[df['Age'] > 0] | |
| df = df[df['Session_Duration_Minutes'] >= 0] | |
| df = df[df['Delivery_Time_Days'] >= 0] | |
| df = df.drop_duplicates() | |
| #️⃣ 3. Outlier Detection & Handling (IQR Method) | |
| Method | |
| The IQR method was used: | |
| IQR = Q3 – Q1 | |
| Outliers are values outside: | |
| lower_bound = Q1 - 1.5*IQR | |
| upper_bound = Q3 + 1.5*IQR | |
| Columns Checked | |
| Total_Amount | |
| Unit_Price | |
| Session_Duration_Minutes | |
| Decision | |
| High values in this dataset represent real large purchases. | |
| Therefore, all outliers were kept. | |
| Code | |
| cols = ['Total_Amount', 'Unit_Price', 'Session_Duration_Minutes'] | |
| for col in cols: | |
| Q1 = df[col].quantile(0.25) | |
| Q3 = df[col].quantile(0.75) | |
| IQR = Q3 - Q1 | |
| lower = Q1 - 1.5 * IQR | |
| upper = Q3 + 1.5 * IQR | |
| outliers = df[(df[col] < lower) | (df[col] > upper)] | |
| print(col, "outliers:", outliers.shape[0]) | |
| #️⃣ 4. Descriptive Statistics | |
| Key Findings | |
| Average customer age ≈ 35. | |
| Unit_Price & Total_Amount have a big gap between mean and median → some very expensive items affect the mean. | |
| Quantity median = 2 → most purchases are small. | |
| Average rating ≈ 4. | |
| Correlations | |
| Unit Price ↔ Total Amount: strong correlation (~0.79) | |
| Quantity ↔ Total Amount: moderate correlation (~0.33) | |
| Most other correlations are weak. | |
| Code | |
| df.describe() | |
| mean_values = df.mean(numeric_only=True) | |
| median_values = df.median(numeric_only=True) | |
| correlations = df.corr(numeric_only=True) | |
| print(mean_values) | |
| print(median_values) | |
| print(correlations) | |
| #️⃣ 5. Visualizations | |
| Age Distribution | |
| Total Purchase Amount Distribution | |
| Unit Price vs Total Amount | |
| Session Duration Distribution | |
| #️⃣ 6. Research Questions & Answers | |
| Q1: Is there a relationship between session duration and total purchase amount? | |
| sns.scatterplot(x=df['Session_Duration_Minutes'], y=df['Total_Amount']) | |
| Insight: | |
| Longer session duration tends to correlate with slightly higher purchase amounts. | |
| Q2: Do returning customers spend more than new customers? | |
| avg_spending = df.groupby('Is_Returning_Customer')['Total_Amount'].mean() | |
| Insight: | |
| Returning customers spend more on average. | |
| Q3: Is there a relationship between quantity and total purchase amount? | |
| sns.scatterplot(x=df['Quantity'], y=df['Total_Amount']) | |
| Insight: | |
| As quantity increases, total amount increases—moderate correlation. | |
| Q4: What is the age distribution of customers? | |
| sns.histplot(df['Age'], bins=20, kde=True) | |
| Insight: | |
| Most customers are between 25–45, suggesting that the store’s main demographic is adults in their working years. | |
| #️⃣ 7. Key Insights | |
| Returning customers contribute significantly more revenue. | |
| Session duration has a mild but visible impact on purchase amounts. | |
| Price distribution suggests a few very expensive items dominating averages. | |
| Age distribution centered around 30–40 → strong, predictable target group. | |
| #️⃣ 8. Decisions Made | |
| Kept outliers, as they represent real large purchases. | |
| Fixed date format to enable time-based analysis. | |
| Removed invalid values (negative numbers, if present). | |
| No duplicates → dataset kept intact. | |
| #️⃣ 9. Files Included | |
| ecommerce_customer_behavior_dataset.csv — original dataset | |
| Assignment_EDA.ipynb — notebook with full analysis | |
| README.md — this file | |
| ## 📹 Presentation Video | |
| A short presentation video summarizing the dataset, the EDA process, key insights, and lessons learned is included as part of this submission. | |
| You can watch it here: | |
| **[▶️ Click to view the presentation video](video1129803838.mp4)** | |
| #️⃣ 10. Final Notes (Reflections) | |
| This project demonstrated the importance of structured EDA before modeling. | |
| Challenges included choosing meaningful questions and validating numeric values. | |
| The analysis helped uncover core behavioral patterns that can directly support future classification tasks. | |