Ravichandran a

To Build a machine learning model that predicts whether an online customer of a retail shop will make their next purchase 90 days from the last purchaseTo Build a machine learning model that predicts whether an online customer of a retail shop will make their next purchase 90 days from the last purchase

Skills: Python (Programming Language) · Machine Learning · Exploratory Data Analysis · Data Cleaning

GitHub - ravichandranECE/Machine-learning-projects

Contribute to ravichandranECE/Machine-learning-projects development by creating an account on GitHub.

https://github.com/ravichandranECE/Machine-learning-projects

We intend to develop a comprehensive predictive model for lung cancer risk assessment using a dataset enriched with a wide array of patients attributes, including gender,age, smoking history and spectrum of health related indicators.
PROJECT WOKRFLOW
Data wrangling
Data cleaning 
Data preprocessing
Outlier and Satistics analysis
Exploratory Data Analysis
Hypothesis Testing 
Model Building
Hyperparameter Tuning for model selection
Model selection.
GUI development for prediction
conclusionWe intend to develop a comprehensive predictive model for lung cancer risk assessment using a dataset enriched with a wide array of patients attributes, including gender,age, smoking history and spectrum of health related indicators. PROJECT WOKRFLOW Data wrangling Data cleaning Data preprocessing Outlier and Satistics analysis Exploratory Data Analysis Hypothesis Testing Model Building Hyperparameter Tuning for model selection Model selection. GUI development for prediction conclusion

Skills: GUI development · Data Visualization · Tkinter · Python (Programming Language) · Hypothesis Testing · Machine Learning · Exploratory Data Analysis · Satistics

GitHub - ravichandranECE/Machine-learning-projects

Contribute to ravichandranECE/Machine-learning-projects development by creating an account on GitHub.

https://github.com/ravichandranECE/Machine-learning-projects

To create an efficient Machine learning models to predict the patient has Heart disease or not with the given labelled data set

PROJECT WORKFLOW
Data wrangling
Data cleaning
Outlier and statistics analysis
Exploratory Data Analysis
Hypothesis Testing
Data preprocessing 
Model Building
Hyperparameter Tuning for model selection
Best Model selection
Tkinter GUI development for prediction
ConclusionTo create an efficient Machine learning models to predict the patient has Heart disease or not with the given labelled data set PROJECT WORKFLOW Data wrangling Data cleaning Outlier and statistics analysis Exploratory Data Analysis Hypothesis Testing Data preprocessing Model Building Hyperparameter Tuning for model selection Best Model selection Tkinter GUI development for prediction Conclusion

Skills: GUI development · Data Visualization · Tkinter · Python (Programming Language) · Data Analysis · Hypothesis Testing · Statistics · Machine Learning · Exploratory Data Analysis

GitHub - ravichandranECE/Machine-learning-projects

Contribute to ravichandranECE/Machine-learning-projects development by creating an account on GitHub.

https://github.com/ravichandranECE/Machine-learning-projects

The focus is on predicting the quality of wine based on its chemical characteristics, offering a real-world application of machine learning in the context of viticulture. The dataset encompasses diverse chemical attributes, including density and acidity, which serve as the features for classifier models.

Data-Analysis-in-Python/PUBG player analysis at main · ravichandranECE/Data-Analysis-in-Python

Contribute to ravichandranECE/Data-Analysis-in-Python development by creating an account on GitHub.

https://github.com/ravichandranECE/Data-Analysis-in-Python/tree/main/PUBG%20player%20analysis

Data wrangling-Data cleaning-data preprocessing-EDA-statistical analysis-model building

Data-Analysis-in-Python/PUBG player analysis at main · ravichandranECE/Data-Analysis-in-Python

Contribute to ravichandranECE/Data-Analysis-in-Python development by creating an account on GitHub.

https://github.com/ravichandranECE/Data-Analysis-in-Python/tree/main/PUBG%20player%20analysis

This project aims to create an analytics dashboard for
retail businesses to analyze sales trends, customer behavior,
and store performance.
The dataset includes sales data, customer profiles, 
and store information. Preprocessing involves aggregating 
sales data, calculating customer metrics. Visualizations can reveal insights into customer demographics, popular products, and peak shopping hours, enabling retailers to optimize inventory, plan marketing campaigns, and enhance customer experiences.
@Bostoninstituteofanalytics

See immediate improvements in decision-making, forecasting accuracy, and overall sales performance. The future of your sales success is just a dashboard away!

🔍 Key Features:
Real-time sales updates 🔄
Comprehensive sales pipeline analysis 📊
Customer segmentation for targeted strategies 🎯
Visualize trends with intuitive charts 📈
Drill-down capabilities for deeper insights 🔍

Dashboard overview:

1. YTD sales weekly trend : Display line chart illustrating the weekly trend of ytd sales.
2.YTD total sales by body style : visualize the distribution of ytd total sales across different car body styles using a pie chart.
3.YTD total sales by color : Present the contribution of various car colors to the YTD total sales through a pie chart.
4.YTD total sales by dealer region : showcase the ytd sales data based on different dealer region using a map chart to visualize the sales distribution geographically.
5.comapny wise sales trend in grid form : provide a tabular grid that display the sales trend for each company.
6.Details grid showing all car sales information : create a detailed grid that present all relevant information for each car sales.

Visualizations can reveal insights into customer demographics, 
 popular products, and peak shopping hours, enabling retailers to optimize 
 inventory, plan marketing campaigns, and enhance customer experiences.

See immediate improvements in decision-making, forecasting accuracy, and overall sales performance. The future of your sales success is just a dashboard away!


Title: The dashboard is titled “PIZZA SALES REPORT.”
Sections:
Total Revenue:
Average Order Values:
Total Orders:
Total Pizzas Sold:
Average Pizzas Per Order:
Trends 💥
Daily Trends for Total Orders:
Monthly Trends for Total Orders:
Sales by Pizza Category:
Sales by Pizza Size:
Total Pizza Sold by Category:

Additional Insights ‼ :
Busiest Days & Times: Orders peak on weekends (Thursday and Friday), and there’s a spike in orders during January and July.
Sales Performance: The Classic category contributes significantly to both sales and total orders, while the Large size is the top contributor to sales.

Remember, this dashboard provides valuable insights for optimizing pizza sales. Whether you’re a pizza enthusiast or a business owner, these trends can guide your decisions! 🍕📊