IELTS Success 🎉

IELTS Success Analysis and Prediction/Dataset/README.md

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+# IELTS Success Stories Dataset 
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, [IELTS Success Stories Dataset](https://www.kaggle.com/datasets/zakirkhanaleemi/ielts-success-stories-dataset)
+
+## About the dataset
+
+- There are 27 rows / entries in this dataset.
+- There are 23 different features which are listed below:
+
+ - Candidate: Name or identifier of the individual who took the IELTS test.
+ - Location: The city or region where the candidate is located.
+ - Profession: The candidate's occupation or field of work/study.
+ - Study Duration (months): The duration, in months, that the candidate spent preparing for the IELTS test.
+ - IELTS Score (Overall): The overall band score achieved by the candidate in the IELTS test.
+ - Key Strategies: Strategies and methods employed by the candidate during their IELTS preparation.
+ - Education Level: The highest level of education attained by the candidate (e.g., Bachelor's, Master's).
+ - Age: The age of the candidate at the time of taking the IELTS test.
+ - Target Country: The country the candidate aspires to move to or pursue further studies in.
+ - English Proficiency (Preparation): The candidate's self-assessed English proficiency level before starting IELTS preparation.
+ - Practice Hours per Week: The average number of hours per week the candidate dedicated to IELTS practice.
+ - Mock Tests Taken: The number of practice/mock IELTS tests taken by the candidate.
+ - Achieved Desired Score: Indicates whether the candidate achieved their target IELTS score.
+ - Preferred Learning Resources: The materials or resources the candidate favored during their IELTS preparation.
+ - Application Status: The status of the candidate's application for further studies or immigration.
+ - Job Offer Received: Indicates whether the candidate received a job offer in their target country.
+ - Additional Certifications: Any additional certifications or qualifications attained by the candidate.
+ - Volunteer Experience: Whether the candidate has relevant volunteer experience.
+ - Language Fluency: The candidate's fluency in languages other than English.
+ - Internship Experience: Whether the candidate has relevant internship experience.
+ - Relevant Skills: Skills possessed by the candidate that are relevant to their profession or studies.
+ - Recommendations: The strength of recommendations provided for the candidate.
+ - Networking Efforts: Efforts made by the candidate to network within their field or community.
+

IELTS Success Analysis and Prediction/README.md

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+<h1>IELTS Success Stories Analysis and Prediction Model</h1>
+
+**GOAL**
+
+The aim of this project is to analyze and predict the success rates of IELTS.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/zakirkhanaleemi/ielts-success-stories-dataset
+
+**DESCRIPTION**
+
+To analyze the IELTS Success Stories Dataset and build and train the model on the basis of different features and variables.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="heatmap" src="./Images/correlation_heatmap.png">
+
+<img alt="graph" src="./Images/target_correlation.png">
+
+<img alt="graph" src="./Images/Application Status_feature.png">
+
+<img alt="graph" src="./Images/Location_feature.png">
+
+<img alt="graph" src="./Images/Study Duration (months)_feature.png">
+
+
+**MODELS USED**
+
+| Model | MSE_train | R2_train | MSE_test | R2_test |
+|-----------------------------|---------------------|----------|-----------|-----------|
+| Random Forest Regression | 7.79e-03 | 0.977 | 0.0151 | 0.9257 |
+| XG Boost Regression | 1.42e-07 | 1.000 | 0.0165 | 0.919 |
+| Decision Tree Regression | 0.000 | 1.000 | 0.0208 | 0.8974 |
+| Ridge Regression | 6.44e-04 | 0.998 | 0.0723 | 0.6439 |
+| Elastic Net Regression | 9.25e-02 | 0.727 | 0.1335 | 0.3428 |
+| Linear Regression | 4.13e-30 | 1.000 | 0.154 | 0.2418 |
+| KNN Regression | 1.01e-01 | 0.703 | 0.1683 | 0.1713 |
+
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which contains 27 entries in it and having 23 features 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 and plot the box-plot to see the distribution of dataset correspond to target features.
+* Split the dataset into training and testing dataset.
+* Apply PCA to reduce the number of features.
+* Apply different training models and get their accuracies and MSE and R2 scores.
+* 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**
+
+- Random Forest and XG Boost 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.
+
+
+**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/) [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Avdhesh-Varshney)
+

IELTS Success Analysis and Prediction/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