F1 Visa Experiences

F1 Visa Experiences/Model/README.md

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+**F1 Visa Experiences**
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+**GOAL**
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+Finding out if the review is positive, negative or neutral.
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+**DATASET**
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+https://www.kaggle.com/datasets/adiamaan/f1-visa-experiences
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+**WORK DONE**
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+* Analyzed the data and found insights and plotted graphs accordingly etc.
+* Preprocessed the data to make it fit for training for ML models.
+* Next trained model with algorithms with default parameters:
+	* Logistic Regression
+	* Linear SVM
+	* Random Forest
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+* In this, Support Vector Machine(SVM) performed the best with 97.27% accuracy. (Refer : `visa_experience.ipynb`)
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+**MODELS USED**
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+1. Logistic Regression : Logistic regression is easier to implement, interpret, and very efficient to train. It is **very fast at classifying unknown records**.
+2. Linear SVM : SVM performs well on classification problems when size of dataset is not too large.
+3. Random Forest : It **provides higher accuracy through cross validation**. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data. If there are more trees, it won't allow over-fitting trees in the model.
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+**LIBRARIES NEEDED**
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+* Numpy
+* Pandas
+* Matplotlib
+* scikit-learn
+* nltk
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+**PLOTS**
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+![Model Accuracies](../Images/final_accuracy.png "Model Accuracies")
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+**CONCLUSION**
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+We analyse the data, preprocess and visualize the features. We then investigated two predictive models. The data was split into two parts, a train set and a test set.
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+We started with Logistic Regression, Random Forest Classifier and SVM and SVM had the highest accuracy followed by Random Forest Classifier.
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+**CONTRIBUTION BY**
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+*Churnika S Mundas*
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+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/churnika-mundas-64767b246/) [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/stackaway)