Datasets:

sujal7102003
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dataset

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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: `train.csv`
-### 📌 Features:
-- `danceability`: How suitable a track is for dancing.
-- `energy`: Intensity and activity level of a track.
-- `key`: Musical key (integer from 0 to 11).
-- `loudness`: Overall loudness in decibels (dB).
-- `mode`: Major (1) or minor (0) tonality.
-- `speechiness`: Presence of spoken words in the track.
-- `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 conveyed by a track.
-- `tempo`: Beats per minute (BPM) of the track.
-- `duration_ms`: Track duration in milliseconds.
-- `time_signature`: Estimated time signature (e.g., 3, 4, 5).
-- `liked`: User preference score (continuous numeric value).
 ---
 ## 🧼 Data Preprocessing
-- Checked for missing values and handled them.
-- Identified and removed duplicate records.
-- Normalized numeric features using **Z-score normalization**.
-- Examined outliers using box plots.
-- Computed descriptive statistics: **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](danceability_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 are approximately normally distributed.
-- Strong positive correlation found between **energy**, **valence**, and the `liked` score.
-- Songs with higher **danceability** and **valence** tend to have higher user preference scores.
-- Some features (e.g., **key**, **mode**) show low correlation with the target variable.
 ---
-## 🛠️ Tools Used
-- Python
-- pandas, numpy
-- matplotlib, seaborn
-- scikit-learn
 ---
-## 🔚 Conclusion
-This analysis highlights key audio features influencing user preferences. These insights may be useful for music recommendation systems or user profiling based on listening history.

 # 🎵 Music Feature Dataset Analysis
+Welcome to the Exploratory Data Analysis (EDA) of a music feature dataset. This project aims to uncover insights into how audio features of songs influence user preferences, using the `liked` column as the target variable.
 ---
+## 📁 Dataset Overview: `train.csv`
+This dataset contains various musical/audio features for tracks, along with a `liked` score representing user preference.
+### 🎼 Features Description
+| Feature           | Description                                                           |
+|-------------------|------------------------------------------------------------------------|
+| `danceability`    | Suitability of a track for dancing.                                    |
+| `energy`          | Intensity and activity level of the track.                             |
+| `key`             | Musical key as an integer (0 to 11).                                   |
+| `loudness`        | Overall loudness in decibels (dB).                                     |
+| `mode`            | Tonality: major (1) or minor (0).                                      |
+| `speechiness`     | Presence of spoken words in the track.                                 |
+| `acousticness`    | Likelihood the track is acoustic.                                      |
+| `instrumentalness`| Predicts whether a track contains no vocals.                           |
+| `liveness`        | Presence of an audience in the recording.                              |
+| `valence`         | Positiveness conveyed by the music.                                    |
+| `tempo`           | Tempo in beats per minute (BPM).                                       |
+| `duration_ms`     | Duration of the track in milliseconds.                                 |
+| `time_signature`  | Estimated time signature (usually 3, 4, or 5).                         |
+| `liked`           | User preference score (continuous numerical value).                    |
 ---
 ## 🧼 Data Preprocessing
+- ✅ Handled missing values
+- ✅ Removed duplicates
+- ✅ Normalized numeric features using **Z-score normalization**
+- ✅ Detected outliers using box plots
+- ✅ Computed summary statistics (mean, std, etc.)
 ---
+## 📊 Visual Analysis
+Visualizations included in the analysis:
+| Visualization                     | Description                                  |
+|----------------------------------|----------------------------------------------|
+| ![Missing Values](missing_values_graph.png)         | Heatmap of missing values                     |
+| ![Unique and Duplicated](unique_duplicated.png)     | Count of unique and duplicate rows            |
+| ![Z-Score](zscore.png)                             | Normalized feature distributions              |
+| ![Top & Bottom Liked](top_bottom_liked.png)         | Most and least liked tracks                   |
+| ![Distribution](distribution.png)                   | Histograms of key features                    |
+| ![Danceability Histogram](danceability_histogram.png)| Focused view on `danceability` distribution   |
+| ![Mean and STD](mean_std_plot.png)                  | Feature-wise mean and standard deviation      |
+| ![Correlation Heatmap](correlation_heatmap.png)     | Feature correlation with `liked`              |
+| ![Boxplot](boxplot.png)                             | Outlier detection via box plots               |
+---
+## 📈 Key Findings
+- 🔹 Features like `energy`, `valence`, and `danceability` have **positive correlation** with the `liked` score.
+- 🔹 Features like `key` and `mode` show **low or no correlation** with user preference.
+- 🔹 Most features are approximately **normally distributed**.
+- 🔹 Tracks that are **highly energetic, positive, and danceable** are generally more liked.
+---
+## 🛠️ Tools & Libraries Used
+- **Language**: Python 🐍
+- **Libraries**:
+  - `pandas`, `numpy` for data handling
+  - `matplotlib`, `seaborn` for visualizations
+  - `scikit-learn` for preprocessing
 ---
+## 📌 Conclusion
+This analysis helps identify which audio features most impact user preferences. These insights can guide the development of:
+- 🎧 Music recommendation systems
+- 📊 User behavior models
+- 🤖 Feature engineering in ML projects
 ---
+## 👤 Author
+**Sujal Thakkar**
 ---