My Story
- Professional qualified Data Scientist/Data Analyst with around 6 years of experience in Data Science and Analytics, including Data Mining, Machine Learning, Statistical Analysis and SQL.
- Involved in the entire data science life cycle and actively engaged in all the phases, including data cleaning, data extraction, and data visualization with large data sets of structured and unstructured data
- Experienced with Supervised machine learning algorithms and Un-Supervised machine learning algorithms
- Implemented entire Life Cycle of Machine Learning, Gathering Data, Data preparation, Data Wrangling, Analyze Data, Train the model, & test the model
- Solid ability to develop Supervised Machine Learning Algorithms of Classification, Regression, and Unsupervised Learning Algorithm, Clustering and Association
- Excellent understanding Data Pre-processing steps, Getting the Dataset, Importing Libraries, Importing Datasets, Finding Missing Data, Encoding Categorical Data, LableEncoder, OneHotEncoder, Splitting Dataset into Training and Test Set, Feature Scaling, Standardization & Normalization.
- Skilled in Classification Algorithms with Linear Models: Logistic Regression, Support Vector Machines, Non-linear Models: K-Nearest Neighbors, Naïve Bayes, Decision Tree Classification, Random Forest Classification, Kernel SVM
My Skills
Domain Expertise
- Banking
- Mining Analysis
- Doc Classification
- Sentiment Analysis
- Anomaly Detection
- Speech Recognition
- Face Detection
- Q & A System
Problem statement:
The objective of this project is to develop a credit card fraud detection system using the dataset provided by the client.
The dataset contains many numbers of credit card transactions, with both fraudulent and non-fraudulent cases.
The goal is to build a machine learning model that can accurately identify fraudulent transactions to help customers of the bank from the risk and minimize losses due to fraudulent activities.
It is important that credit card companies can recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase
Responsibilities:
Involved in entire data science and machine learning life cycle process like data gathering, data augmentation, visualizing the data points
Encoding the data and setting the dataset for training and testing
Building the model with the dataset using a classification algorithms like logistic regression and random forest classifier to find weather the transactions are fraudulent or non-fraudulent.
Validating the model by applying the metrics like f1 score for finding the accuracy, if the performance is not good need to improve by doing hyper-parameter tuning on the model to achieve best predictions.
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