Merge pull request #577 from stackaway/main

F1 Visa Experiences
abhisheks008 · Feb 10, 2024 · 4cdae8a · 4cdae8a
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diff --git a/F1 Visa Experiences/Dataset/telegram.csv b/F1 Visa Experiences/Dataset/telegram.csv
diff --git a/F1 Visa Experiences/Images/final_accuracy.png b/F1 Visa Experiences/Images/final_accuracy.png
diff --git a/F1 Visa Experiences/Images/output.png b/F1 Visa Experiences/Images/output.png
diff --git a/F1 Visa Experiences/Images/output_model2.png b/F1 Visa Experiences/Images/output_model2.png
diff --git a/F1 Visa Experiences/Images/output_model3.png b/F1 Visa Experiences/Images/output_model3.png
diff --git a/F1 Visa Experiences/Images/sentiment_score.png b/F1 Visa Experiences/Images/sentiment_score.png
diff --git a/F1 Visa Experiences/Model/README.md b/F1 Visa Experiences/Model/README.md
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+**F1 Visa Experiences**
+
+**GOAL**
+
+Finding out if the review is positive, negative or neutral.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/adiamaan/f1-visa-experiences
+
+**DESCRIPTION**
+
+This dataset contains Visa interview experiences from about 6391 users, who are students applying to live temporarily in the US while studying at a school. The data comes from a telegram channel and all the visa experiences mainly are from India. 
+
+**WHAT I HAD DONE**
+
+* Analyzed data, extracted insights, and generated relevant visualizations.
+* Preprocessed data to prepare it for machine learning model training.
+* Trained default-parameter models:
+		* Logistic Regression
+		* Linear SVM
+		* Random Forest
+
+* In this, Support Vector Machine(SVM) performed the best with 97.27% accuracy. (Refer : `visa_experience.ipynb`)
+
+**MODELS USED**
+
+* Logistic Regression
+* Linear SVM
+* Random Forest
+
+**LIBRARIES NEEDED**
+
+* Pandas        V2.0.3
+* Numpy         V1.24.3
+* Matplotlib    V3.7.2
+* Scikit-learn  V1.3.2
+* nltk          V3.8.1
+
+**VISUALIZATION**
+
+![Sentiment Score](../Images/sentiment_score.png "Sentiment Score")
+
+**ACCURACIES**
+
+	* Logistic Regression - 94.46
+	* Linear SVM		  - 94.92
+	* Random Forest		  - 97.27
+
+**CONCLUSION**
+
+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.
+
+We started with Logistic Regression, Random Forest Classifier and SVM and SVM had the highest accuracy followed by Random Forest Classifier.
+
+
+**YOUR NAME**
+
+*Churnika S Mundas*
+
+
+[![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)