Exploratory data analysis and Machine learning model
Deployed Machine learning Model
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Exploratory data analysis with Machine learning
Click on each heading to view Exploratory data analysis with Machine learning on different domains.
Accounts Receivable Understand the factors of successful collection efforts. You can Predict which customers will pay fastest and recover more money and improve collections efficiency.
Sales Analysis and Prediction:XGBOOST
Predict total sales for every product and store in the next month
Shopping Mall Customer Segmentation Analysis
Analyze to understand the customers like who can be easily converge (Target Customers)
Sales Prediction: LSTMwith GridSearch
Predict total sales for time-series dataset consisting of daily sales data
Employee Attrition_Analysis and Prediction
Uncover the factors that lead to employee attrition
Marketing Campaign-Prospective Customers
Explore data collected during marketing campaign and predict is customer is willing to buy bank product.
House Price prediction :Boosting+Stacking
Analyze the data set of house size, condition and location and predict the price.
Analyze the passenger detail and predict what sorts of people were more likely to survive.
Deployed Machine learning Model
Salary Calculation Based on Experience
Simple Linear regression models are used to show or predict the relationship between two variables. In linear regression, each data set consists of two values. One value is for the dependent variable and one value is for the independent variable. In this data model our dependent variable is "Salary" and independent variable is "Experience". The model has been trained now and ready for predicting salary.
Predict Profit for Start Up Company
As a predictive analysis, the multiple linear regression is used to explain the relationship between one continuous dependent variable and two or more independent variables. The independent variables can be numerical or categorical value.
This data set has data collected from New York, California and Florida about 50 business Startups "17 in each state". The variables used in the data set are R&D spending, Administration Spending, Marketing Spending and Profit. The problem statement is we need to predict the profit(dependent variable) based on R&D spending, Administration Spending, Marketing Spending.
Light GBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithm, used for ranking, classification and many other machine learning tasks.
We need to predict the price of house based on the given parameter.