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+# 🎵 Music Feature Dataset Analysis
+
+This repository contains an exploratory data analysis (EDA) on a music features dataset. The objective is to understand the patterns in audio features and how they relate to the user's preferences (`liked` column).
+
+---
+
+## 📁 Dataset: `data.csv`
+
+### 📌 Features:
+
+- `danceability`: How suitable a track is for dancing.
+- `energy`: Intensity and activity level of a track.
+- `key`: Musical key.
+- `loudness`: Overall loudness in decibels (dB).
+- `mode`: Major or minor tonality.
+- `speechiness`: Presence of spoken words.
+- `acousticness`: Confidence measure of whether the track is acoustic.
+- `instrumentalness`: Predicts if a track is instrumental.
+- `liveness`: Presence of audience in the recording.
+- `valence`: Musical positiveness.
+- `tempo`: BPM of the track.
+- `duration_ms`: Track duration in milliseconds.
+- `time_signature`: Estimated time signature.
+- `liked`: User preference (continuous score).
+
+---
+
+## 🧼 Data Preprocessing
+
+- ✅ Checked for missing values.
+- ✅ Identified and removed duplicates.
+- ✅ Normalized numeric features using **Z-score**.
+- ✅ Outliers examined using box plots.
+- ✅ Computed statistical metrics: **mean**, **standard deviation**, etc.
+
+---
+
+## 📊 Visualizations
+
+### 🔹 Missing Values
+
+![Missing Values](missing_values_graph.png)
+
+---
+
+### 🔹 Unique & Duplicated Records
+
+![Unique and Duplicated](unique_duplicated.png)
+
+---
+
+### 🔹 Z-score Normalization
+
+![Z-Score](zscore.png)
+
+---
+
+### 🔹 Top & Bottom Liked Songs
+
+![Top & Bottom Liked](top_bottom_liked.png)
+
+---
+
+### 🔹 Distribution Plots
+
+![Distribution](distribution.png)
+
+---
+
+### 🔹 Danceability Histogram
+
+![Danceability Histogram](danceblity_histogram.png)
+
+---
+
+### 🔹 Mean & Standard Deviation
+
+![Mean and STD](mean_std_plot.png)
+
+---
+
+### 🔹 Correlation Heatmap
+
+![Correlation Heatmap](correlation_heatmap.png)
+
+---
+
+### 🔹 Boxplots for Outlier Detection
+
+![Boxplot](boxplot.png)
+
+---
+
+## 📈 Summary
+
+- Most features were normally distributed.
+- Strong correlation found between **energy**, **valence**, and **liked** score.
+- Songs with higher **danceability** and **valence** tended to have higher `liked` scores.
+- Some features (like **key** and **mode**) had low correlation with the target variable.
+
+---
+
+## 🛠️ Tools Used
+
+- `Python`
+- `Pandas`, `NumPy`
+- `Matplotlib`, `Seaborn`
+- `Scikit-learn`
+
+---
+
+## 🔚 Conclusion
+
+The analysis highlights key audio features that influence user preferences. The results may be useful for recommendation systems or user profiling based on listening history.
+
+---
+