Top foreign language analysis #519

Top Foreign Languages Analysis/Dataset/README.md

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+# Top Foreign Languages Dataset
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, Top Foreign Languagess Analysis and Prediction.( https://www.kaggle.com/datasets/timmofeyy/top-foreign-languages-preply-tutors)
+
+## About the dataset
+
+The data includes the main languages and the most popular among students. The datasets have 8 csv files for 8 different top foreign languages.
+
+- **columns_description**: Each CSV File contains the description of all the features.
+
+  name: The name of the tutor.
+
+  badge: Any badge or certification associated with the tutor.
+
+  rating: The overall rating of the tutor.
+
+  reviews_number: The number of reviews the tutor has received.
+
+  usd_price: The price charged by the tutor for their services.
+
+  language: The languages spoken by the tutor.
+
+  active_students: The number of active students the tutor is currently teaching.
+
+  lessons_number: The total number of lessons conducted by the tutor.
+
+  speak: The languages spoken by the tutor.
+
+  description: A brief description or snippet provided by the tutor.
+
+  link: The link or URL to the tutor's profile.

Top Foreign Languages Analysis/README.md

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+<h1>Top Foreign Language Analysis</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the usd_price per lesson for a given language and other feature.
+
+**DATASET**
+
+[https://www.kaggle.com/datasets/timmofeyy/top-foreign-languages-preply-tutors]
+
+**DESCRIPTION**
+
+To analyze the dataset of Top Foreign Language and build and train the model on the basis of different features and variables.
+
+The data includes the main languages and the most popular among students. The datasets have 8 csv files for 8 different top foreign languages.
+
+- **columns_description**: Each CSV File contains the description of all the features.
+
+  name: The name of the tutor.
+
+  badge: Any badge or certification associated with the tutor.
+
+  rating: The overall rating of the tutor.
+
+  reviews_number: The number of reviews the tutor has received.
+
+  usd_price: The price charged by the tutor for their services.
+
+  language: The languages spoken by the tutor.
+
+  active_students: The number of active students the tutor is currently teaching.
+
+  lessons_number: The total number of lessons conducted by the tutor.
+
+  speak: The languages spoken by the tutor.
+
+  description: A brief description or snippet provided by the tutor.
+
+  link: The link or URL to the tutor's profile.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="correlation" src="./Images/correlation between amount of lessons and price per lesson.png">
+
+<img alt="graph" src="./Images/count of tutors.png">
+
+<img alt="graph" src="./Images/distribution plot.png">
+
+
+
+
+**MODELS USED**
+
+| Model                     | MSE_train | R2_train | MSE_test  | R2_test   |
+|---------------------------|-----------|----------|-----------|-----------|
+| Random Forest Regression  | 7.03      | 0.93     | 57.90     | 0.51      |
+| Linear Regression         | 17.09     | 0.84     | 60.72     | 0.49      |
+| Ridge Regression          | 85.65     | 0.22     | 96.16     | 0.20      |
+| Elastic Net Regression    | 105.0     | 0.04     | 114.7     | 0.047     |
+| Decision Tree Regression  | 0.00      | 1.00     | 61.30     | 0.49      |
+| Deep NN                   | 34.29     | 0.04     | 114.7     | 0.0471    |
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which is in zip file format unzipped it and than concatenated all 8 CSV files.
+* After Concatenation it contains 34442 entries in it and having 47 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
+10. missingno
+11. plotly
+
+
+**CONCLUSION**
+
+- Random Forest and Linear Regression models show promising performance with lower MSE and higher R2 values.
+- Decision Tree Regression achieved perfect R2 on the training set but performed poorly on the test set, indicating overfitting.
+- Deep Neural Network (NN) has a high MSE and approximately zero R2, suggesting poor performance on both training and test sets.
+
+
+**YOUR NAME**
+
+*Ghousiya Begum*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ghousiya-begum-a9b634258/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ghousiya47)
+

Top Foreign Languages Analysis/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
+missingno==0.5.2
+plotly==5.15.0