Merge pull request #489 from Avdhesh-Varshney/gender

Gender Recognition By Voice Model

Gender Recognition By Voice/Dataset/README.md

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+The Dataset is used here is taken from the Kaggle database website. You can download the file from the link given here, [Gender Recognition By Voice](https://www.kaggle.com/datasets/alarmanovi/gender-recognition-by-voice-2023)
+
+I have also attached the dataset in this folder to download from here directly [Link](./Male%20and%20female%20Voice%20data%20creat%20by%20al%20arman%20ovi%20.csv)
+

Gender Recognition By Voice/README.md

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+<h1>Gender Recognition By Voice</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the voice based on their genders.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/alarmanovi/gender-recognition-by-voice-2023
+
+**DESCRIPTION**
+
+To analyze the dataset of Gender recognition by voice 2023 and build and train the model on the basis of their voice and predict the genders.
+
+### Visualization and EDA of different attributes:
+
+<img alt="Screenshot1" src="./Images/1.png">
+
+<img alt="Screenshot2" src="./Images/2.png">
+
+<img alt="Screenshot3" src="./Images/3.png">
+
+<img alt="Screenshot4" src="./Images/4.png">
+
+<img alt="Screenshot5" src="./Images/5.png">
+
+**MODELS USED**
+
+| Model                        | MSE              | R2               |
+|------------------------------|------------------|------------------|
+| Gradient Boosting Regression | 1.406672e-01     | 4.031331e-01     |
+| Random Forest Regression     | 1.435905e-01     | 3.907292e-01     |
+| XG Boost Regression          | 1.505178e-01     | 3.613356e-01     |
+| Linear Regression            | 1.718679e-01     | 2.707449e-01     |
+| Ridge Regression             | 1.809122e-01     | 2.323690e-01     |
+| Elastic Net Regression       | 1.854163e-01     | 2.132574e-01     |
+| Deep NN                      | 2.566216e-01     | -8.887454e-02    |
+| Decision Tree Regression     | 2.777314e-01     | -1.784462e-01    |
+| SGD Regression               | 2.557489e+29     | -1.085172e+30    |
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which contains 5993 entries in it and having 22 columns in it.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Plotting heatmap using correlation and checking the relation between different features.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. XGBoost
+6. Tensorflow
+7. Keras
+8. Sci-py
+9. Seaborn
+
+
+**CONCLUSION**
+
+- Gradient Boosting Regression and Random Forest Regression Models are best fitted to the datasets.
+- MSE and R2 score of both models are very much good compare to other models.
+- The visualization graph also best fitted to the dataset on Gradient Boosting Regression Model.
+
+
+**YOUR NAME**
+
+*Avdhesh Varshney*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/avdhesh-varshney-5314a4233/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Avdhesh-Varshney)
+

Gender Recognition By Voice/requirements.txt

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+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2
+tensorflow==2.4.1
+keras==2.4.0