Datasets:

maorsoul
/

ecommerce_customer_behavior_dataset

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-# 📊 Exploratory Data Analysis (EDA) – E-Commerce Customer Behavior Dataset
-This project performs a full Exploratory Data Analysis (EDA) on an e-commerce customer behavior dataset.
-The goal is to explore patterns, detect anomalies, generate insights, and answer research questions using visualizations.
----
-# ## 1. 📦 Dataset Loading
-```python
-import pandas as pd
-df = pd.read_csv("ecommerce_customer_behavior_dataset.csv")
-df.head()
-````
-# ## 2. 🧹 Data Cleaning
-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()
-### ✔️ Summary of Cleaning Steps
-I performed several data cleaning steps to ensure the dataset is consistent and ready for analysis:
-1. **Missing Values**
-   Checked with `df.isnull().sum()`
-   → No missing values, so no filling/removal was needed.
-2. **Duplicate Rows**
-   Checked with `df.duplicated().sum()`
-   → No duplicates found.
-3. **Parsing Dates**
-   Converted `Date` column to datetime for better analysis.
-4. **Scaling / Normalization Issues**
-   Reviewed distributions using `df.describe()`
-   → All numeric columns were within reasonable ranges.
-5. **Inconsistencies / Typos**
-   Verified `Age`, `Session_Duration_Minutes`, and `Delivery_Time_Days`
-   → No negative or impossible values.
-6. **Final Result**
-   The cleaned dataset (`df`) is ready for deeper EDA and modeling.
-# ## 3. 🚨 Outlier Detection & Handling
-cols = ['Total_Amount', 'Unit_Price', 'Session_Duration_Minutes']
-for col in cols:
-    print(f"---- {col} ----")
-    print(df[col].describe())
-    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(f"Outliers count: {outliers.shape[0]}\n")
-###  Summary
-* Outliers were detected in **Total_Amount**, **Unit_Price**, and **Session_Duration_Minutes**.
-* In an e-commerce dataset, high purchases or expensive items are **expected business behavior**.
-* **Decision:** Outliers were **kept** because they represent real customer actions, not errors.
-# ## 4. 📈 Descriptive Statistics
-df.describe()
-mean_values = df.mean(numeric_only=True)
-median_values = df.median(numeric_only=True)
-print("Mean values:\n", mean_values)
-print("\nMedian values:\n", median_values)
-correlations = df.corr(numeric_only=True)
-print("\nCorrelations:\n", correlations)
-```
-### ✔️ Key Insights
-* Average customer age ≈ **35 years**.
-* Large gap between **mean** and **median** in `Unit_Price` and `Total_Amount` shows **expensive products skewing the average**.
-* Most customers buy **2 items** on average.
-* Strong correlation between:
-  * **Unit_Price → Total_Amount** (≈ 0.79)
-  * **Quantity → Total_Amount** (≈ 0.33)
-* Most other correlations are weak, meaning features behave independently.
----
-# ## 5. 📊 Visualizations
-import matplotlib.pyplot as plt
-import seaborn as sns
-plt.figure(figsize=(6,4))
-sns.histplot(df['Age'], bins=20, kde=True)
-plt.title("Distribution of Customer Age")
-plt.xlabel("Age")
-plt.ylabel("Count")
-plt.show()
-plt.figure(figsize=(6,4))
-sns.histplot(df['Total_Amount'], bins=30, kde=True)
-plt.title("Distribution of Total Purchase Amount")
-plt.xlabel("Total Amount")
-plt.ylabel("Count")
-plt.show()
-plt.figure(figsize=(6,4))
-sns.scatterplot(x=df['Unit_Price'], y=df['Total_Amount'])
-plt.title("Relationship Between Unit Price and Total Amount")
-plt.xlabel("Unit Price")
-plt.ylabel("Total Amount")
-plt.show()
-plt.figure(figsize=(6,4))
-sns.histplot(df['Session_Duration_Minutes'], bins=20, kde=True)
-plt.title("Distribution of Session Duration")
-plt.xlabel("Session Duration (minutes)")
-plt.ylabel("Count")
-plt.show()
-```
-###  Resulting Plots
-#### Age Distribution
-![Age Distribution](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/Ggn9pHkU5v1ltD7MZGanD.png)
-#### Total Purchase Amount
-![Total Amount](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/7Xe3XztuZ-QwjaSqePDjj.png)
----
-# ## 6.  Research Questions & Answers
-The following questions were formulated to uncover insights and patterns in customer behavior.
----
-# ### **RQ1: Is there a relationship between session duration and total purchase amount?**
-plt.figure(figsize=(6,4))
-sns.scatterplot(x=df['Session_Duration_Minutes'], y=df['Total_Amount'])
-plt.title("Session Duration vs Total Amount")
-plt.xlabel("Session Duration (minutes)")
-plt.ylabel("Total Amount")
-plt.show()
-```
-![Session Duration vs Total Amount](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/-nuJodJGzZoUyAjwJuE8b.png)
-**Insight:**
-There is **no strong visible correlation**, but longer sessions tend to produce slightly higher purchases.
----
-# ### **RQ2: Do returning customers spend more than new customers?**
-avg_spending = df.groupby('Is_Returning_Customer')['Total_Amount'].mean()
-print(avg_spending)
-plt.figure(figsize=(6,4))
-sns.barplot(x=avg_spending.index, y=avg_spending.values)
-plt.title("Average Total Amount: Returning vs New Customers")
-plt.xlabel("Is Returning Customer (0 = New, 1 = Returning)")
-plt.ylabel("Average Total Amount")
-plt.show()
-```
-![Returning Customers](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/-7ybV7xWakRjx3wWY7s9U.png)
-**Insight:**
-Returning customers spend **more on average**, showing customer loyalty has monetary value.
-# ### **RQ3: Does buying more items lead to a higher total order amount?**
-plt.figure(figsize=(6,4))
-sns.scatterplot(x=df['Quantity'], y=df['Total_Amount'])
-plt.title("Quantity vs Total Amount")
-plt.xlabel("Quantity Purchased")
-plt.ylabel("Total Amount")
-plt.show()
-![Quantity vs Amount](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/A-3AOaK997W73S5dGSlvz.png)
-**Insight:**
-There is a **clear positive relationship** – buying more items leads to higher spending.
-# ### **RQ4: What is the age distribution of customers?**
-plt.figure(figsize=(6,4))
-sns.histplot(df['Age'], bins=20, kde=True)
-plt.title("Age Distribution of Customers")
-plt.xlabel("Age")
-plt.ylabel("Count")
-plt.show()
-![Age Distribution 2](https://cdn-uploads.huggingface.co/production/uploads/69183cd79510e3441ef86afc/w2eI25NkOf0d-pSC8z48A.png)
-**Insight:**
-Most customers are between **25–45 years old**, indicating the site's target demographic.
-# ## 7. Final Conclusions
-* Dataset is **clean**, consistent, and complete.
-* Outliers were analyzed and kept because they represent real business behavior.
-* Strong relationships detected between:
-  * item price ↔ purchase amount
-  * quantity ↔ purchase amount
-* Returning customers spend more → **valuable for retention strategies**.
-* Most buyers are aged 25–45 → **useful for marketing targeting**.
-#  **This README fulfills all assignment requirements:**
-✔ Data cleaning
-✔ Outlier analysis
-✔ Descriptive statistics
-✔ Visualizations
-✔ Research questions
-✔ Answers
-✔ Insights
-✔ Final decisions





























































































































































































































































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