Gunjan Nath
Data ScientistWhere Technology Meets Art.
Readyly
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Where Technology Meets Art.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Readyly
Data Scientist
June 25, 2026 – Present
Big-Mart-Sales-Prediction-using-Machine-Learning-with-Python
July 4, 2021 – July 4, 2021
Big-Mart-Sales-Prediction-using-Machine-Learning-with-Python — GitHub repository
View ProjectCustomer-Segmentation-using-K-Means-Clustering-Machine-Learning-with-Python
July 4, 2021 – July 4, 2021
This data set is created only for the learning purpose of the customer segmentation concepts , also known as market basket analysis . I will demonstrate this by using unsupervised ML technique (KMeans Clustering Algorithm) in the simplest form. Content You are owing a supermarket mall and through membership cards , you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. Problem Statement You own the mall and want to understand the customers like who can be easily converge [Target Customers] so that the sense can be given to marketing team and plan the strategy accordingly.
View ProjectMedical-Insurance-Cost-Prediction-using-Machine-Learning-with-Python
June 12, 2021 – June 12, 2021
Machine Learning with R by Brett Lantz is a book that provides an introduction to machine learning using R. As far as I can tell, Packt Publishing does not make its datasets available online unless you buy the book and create a user account which can be a problem if you are checking the book out from the library or borrowing the book from a friend. All of these datasets are in the public domain but simply needed some cleaning up and recoding to match the format in the book. Content Columns age: age of primary beneficiary sex: insurance contractor gender, female, male bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9 children: Number of children covered by health insurance / Number of dependents smoker: Smoking region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest. charges: Ind
View ProjectCredit-Card-Fraud-Detection-using-Machine-Learning-with-Python
June 12, 2021 – June 12, 2021
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature
View ProjectHeart-Disease-Prediction-using-Machine-Learning-with-Python
June 12, 2021 – June 12, 2021
This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Content Attribute Information: age sex chest pain type (4 values) resting blood pressure serum cholestoral in mg/dl fasting blood sugar > 120 mg/dl resting electrocardiographic results (values 0,1,2) maximum heart rate achieved exercise induced angina oldpeak = ST depression induced by exercise relative to rest the slope of the peak exercise ST segment number of major vessels (0-3) colored by flourosopy thal: 3 = normal; 6 = fixed defect; 7 = reversable defect The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been "processed", that one containing the Cleveland database. All
View ProjectGold-Price-Prediction-using-Machine-Learning-with-Python
June 12, 2021 – June 12, 2021
Data Overview: This data file is a Comma separated value file format with 2290 rows and 7 columns. It contains 5 columns which are numerical in datatype and one column in Date format. Clearly the data shows value of the variables SPX,GLD,USO,SLV,EUR/USD against the dates in the date column. For this Prediction, I have used a Random Forest Regressor.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic in nature, focusing on common machine learning datasets and problems. While this shows initiative, there is limited evidence of diverse team collaboration, contribution to open-source, or engagement with broader community aspects that typically indicate strong cultural fit for a senior role. The projects are well-aligned with a Data Scientist role, but the breadth of experience beyond standard ML applications is not evident.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate an ability to apply learned concepts, but there is no information on collaboration, problem-solving approach, or communication style.