Readme.md updated

Author: tknishh

Fraudulent Transactions Detection/README.md

@@ -0,0 +1,71 @@
+# Fraudulent Transactions Detection
+
+**AIM**
+
+Develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/miznaaroob/fraudulent-transactions-data
+
+**CONTENT**
+
+Data for the case is available in CSV format having 6362620 rows and 10 columns.
+
+**WHAT I HAD DONE**
+
+First I imported all the required libraries and dataset for this project. Then I did some EDA to find which mode of transaction results into most fraudulent transactions. Then I worked throught to treat any inconsistency in the data. Then I proceeded to build the model. I worked two different models and compared results from both to select mode appropriate one for this project. First I used a logistic regression model classify Fraudulent and Non fraudulent transactions. Next I worked with Random Forest classifier model to amp up the accuracy which resulted in some improvement from the previous LR moel. At the end I observed an accuracy of 99.97.
+
+**MODELS USED**
+
+The models are:
+
+1. Logistic Regression
+2. Random Forest Classifier
+
+**HOW TO RUN**
+
+Upload kaggle api key file and fraud_transaction_detection.ipynb file on colab and just run the code.
+
+**LIBRARIES NEEDED**
+
+* Opendatasets (for downloading the dataset)
+* Pandas - for data analysis
+* Numpy - for data analysis
+* matplotlib - for data visualization
+* seaborn - for data visualization
+* itertools - for data analysis
+
+**VISUALIZATION**
+
+### Dataset Head snipshot
+![Dataset Head snipshot](../Images/Dataset%20head.png)
+
+### Confusion matrix for Logistic Regression model
+![Confusion matrix for Logistic Regression model](../Images/lr_cm.png)
+
+### Confusion matrix for Random Forest Classifier model
+![Confusion matrix for Random Forest Classifier model](../Images/rfc_cm.png)
+
+By viewing Confusion Matrix it is easily deduced that RFC model is the best model for this project.
+
+### Accuracy score for Random Forest Classifier in comparison to Logistic Regression
+![Accuracy score for Random Forest Classifier in comparison to Logistic Regression](../Images/acc_com_lr_rfc.png)
+
+**ACCURACIES**
+
+| Model         | Architecture                      | Accuracy in % (on testing data) |
+| ------------- |:---------------------------------:|:-------------:|
+| Model 1       | Logistic Regression model         |99.83          |
+| Model 2       | Random Forest classifier Model    |99.97          |
+
+
+**CONCLUSION**
+
+I was successfully able to find the most accurate model to detect fraudlent transactions.
+
+# Tanish Khandelwal
+
+Connect with me on Linkedin: https://www.linkedin.com/in/tanish-khandelwal-da/
+
+Check out my Github profile: https://github.com/tknishh