📊 Exploratory Data Analysis (EDA) Project

🛠️ Tech Stack & Libraries

• Language: Python 3.12 | R 4.4
• Core Libraries: pandas, numpy, matplotlib, seaborn
• Optional/Advanced: plotly, ydata-profiling

---

🧹 Data Preprocessing & Cleaning

Before diving into analysis, the data was prepared using the following steps:

• Handling Missing Values: [e.g., dropped nulls, imputed with median, filled via KNN]
• Duplicate Removal: [e.g., removed X duplicated rows]
• Outlier Treatment: [e.g., capped extreme values or removed anomalies]
• Data Type Conversion: [e.g., string dates cast to datetime]

---

📈 Key Findings & Insights

Here are the primary observations uncovered during the exploration process:

• Insight 1: [e.g., Sales peak during Q4, specifically in November.]
• Insight 2: [e.g., Strong positive correlation ((r = 0.85)) between feature A and feature B.]
• Insight 3: [e.g., Uneven distribution in the target variable; data is imbalanced.]

---

📊 Visualizations

Below is a summary of the visualizations used to understand the data's distribution and relationships:

• Univariate Analysis: Histograms and box plots to check individual feature distributions and skewness.
• Bivariate Analysis: Scatter plots and violin plots to identify relationships between the target variable and features.
• Multivariate Analysis: Correlation heatmaps to detect multicollinearity among numerical variables.

---