Viral Shorts Video Analysis

Viral Shorts Video Analysis/Dataset/README.md

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+### DataSet -> https://www.kaggle.com/datasets/kanchana1990/viral-shorts-youtubes-most-viewed
+
+## Dataset Description :
+
+- TITLE
+- PUBLISHED AT
+- DURATION
+- VIEW COUNT
+- LIKES COUNT
+- COMMENTS COUNT
+
+## Created Columns
+
+- TITLE KEYWORDS
+- SHORTS TYPE
+- YEAR
+- MONTH
+- DAY
+
+### Columns use to predict LIKES COUNT are:
+- DURATION
+- VIEW COUNT
+- LIKES COUNT
+- COMMENTS COUNT
+- YEAR
+- MONTH
+- DAY
+
+### Columns Droped are : NA

Viral Shorts Video Analysis/Models/README.md

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+# VIRAL VIDEO SHORTS ANALYSIS
+
+### Aim : The aim of this project is to analyze the viral videos based on the given dataset.
+
+### Data Set : https://www.kaggle.com/datasets/kanchana1990/viral-shorts-youtubes-most-viewed
+
+
+### Work Done : 
+- Analyzed the data and removed those rows having any NULL cell.
+- Plotted graphs to analyze the extract of the data.
+- Lastly tries to predict Number of Likes in viral shorts with the use of features, these features include columns = 'duration', 'year', 'month', 'day', 'comment_count', 'view_count' .
+
+### Library used :
+- Pandas
+- Numpy
+- Natural Language Toolkit
+- String
+- Matplotlib
+- Seaborn
+- Word cloud
+- Scikit learn
+
+### Visual plots :
+
+![](../images/most_common_words.png)
+
+![](../images/word_cloud.png)
+
+![](../images/pair_plot.png)
+
+![](../images/heat_map.png)
+
+![](../images/fequency_of_likes.png)
+
+![](../images/distribution%20of%20video%20duration.png)
+
+![](../images/time_series_of_views.png)
+
+### Model used:
+* Linear Regression
+* Decision Tree Regressor
+* Random Forest Regressor
+* Gradient Boosting Regressor
+* Support Vector Regressor
+* Multi-layer Perceptron Regressor
+* k-Nearest Neighbors Regressor
+
+### Evaluation Result :
+- Test Data Results
+
+| Model                             |Root Mean Square error|    R2 Score      |
+|-----------------------------------|----------------------|------------------|
+| Linear Regression                 | 1,749,841.770        |     0.510        |
+| Decision Tree Regressor           | 1,434,585.424        |     0.671        |
+| Random Forest Regressor           | 1,242,172.244        |     0.753        |
+| Gradient Boosting Regressor       | 1,456,313.340        |     0.661        |
+| Support Vector Regressor          | 2,738,273.404        |    -0.199        |
+| Multi-layer Perceptron Regressor  | 1,799,060.980        |     0.483        |
+| k-Nearest Neighbors Regressor     | 1,582,670.343        |     0.600        |
+
+- Train Data Results
+
+| Model                             |Root Mean Square error|    R2 Score       |
+|-----------------------------------|----------------------|-------------------|
+| Linear Regression                 |    979,192.253       |      0.758        |
+| Decision Tree Regressor           |          0.0         |      1.0          |
+| Random Forest Regressor           |    300,353.851       |      0.977        |
+| Gradient Boosting Regressor       |    282,346.501       |      0.980        |
+| Support Vector Regressor          |  2,111,086.453       |     -0.126        |
+| Multi-layer Perceptron Regressor  |  1,201,344.870       |      0.635        |
+| k-Nearest Neighbors Regressor     |    928,265.630       |      0.782        |
+
+### Conclusion :
+- From given results Random Forest Regressor is best fitted for it.
+- With Train set R2 Score of Decision Tree Regressor was perfect while Random Tree Regressor has more than 97% accuracy.
+
+### Contributor : 
+ *Harsh Raj*
+
+ *Abhishek Sharma* (Mentor)
+
+### Connect with me:
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/harsh-raj-58921728b/) [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/HarshRaj29004)
+