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-arshadulla1-predication-and-deployment-of-adult-dataset-using-classification-and-flask

Visualization and predication of personal income levels as above or below 50,000 per year based on personal details such as relationship and education level using classification model and deployment using flask

analyze-data-of-general-store-using-pivot-table-and-p-l-statement-using-financial-dashboard-in-excel

arduino-based-temperature-controlled-fan

A temperature-controlled fan using Arduino. With this circuit, we will be able to adjust the fan speed in our home or office according to the room temperature and also show the temperature and fan speed changes on an LCD display.

arshadulla1

Config files for my GitHub profile.

assignment

bank-atm-back-end-development-using-python-and-django

Bank ATM Back-End Development using Django and Python and Database "pgadmin4" IDE "Pycharm"

data-visualization-of-bigmart-sales-in-2013

getting insights from the dataset and to understand how various features play a role in increasing the sales using python with data science

prediction-of-ctc-or-salary-using-linear-regression

Strat-Tech Academy Step -I Internship Machine Learning Task 2 Problem Statement 1: You have to create a linear regression model in Python or R to predict the CTC/Salary of new hires from the data provided. Two datasets are provided one is for training and another one is for testing the model by using the linear regression you will predict the CTC of new hires by test dataset given. I used Jupyter Notebook and libraries like Pandas, seaborn, sklearn

voice-controlled-robot-using-arduino

widhya-covid-19-analysis-quantitative-modeling

Read the dataset using the 'read_csv' function in pandas. Then grouped the rows by dates to get the daily case count for Indians and foreigners respectively, recovery count, and death count. used the groupby function in pandas to achieve the above result. the 'sort' argument is used in the groupby function. Store the new data frame in a new variable. After grouping the data, the main aim is to see the daily trends in the spread of total cases. In the dataset, all the features/columns combined give the total count. then sum up each cell in a row and store the result in the data frame as a new feature. This is done using the sum function in pandas. Now visualised the data using matplotlib where the dates are plotted on X-Axis and the total cases are plotted on the Y-Axis.