Repository containing portfolio of data science projects completed by me for academic, self learning, and hobby purposes. Presented in the form of iPython Notebooks.
Note: Data used in the projects (accessed under data directory) is for demonstration purposes only.
1.Install dependencies using requirements.txt.
2.Run notebooks as usual by using a jupyter notebook, server, Vscode etc.
In this prediction help to that farmers they are interested in what is the amount of fertilizers, prestisides and seed is to requrired for maximize the output. Also find the what is the total havesting cost according to your production, and what price are you set per kg if you are selling the market.
That projects to analyze the mobile price to help the new startup company for selecting the price of new mobiles. using Pthon, Flask, PowerBI we can analyse that project.
Unsupervised Learning:Wheat Export analysis: Our objective is to cluster the countries based on various sales data provided to us across years. We have to apply an unsupervised learning technique like K means or Hierarchical clustering so as to get the final solution. But before that we have to bring the exports (in tons) of all countries down to same scale across years. Plus, as this solution needs to be repeatable we will have to do PCA so as to get the principal components which explain max variance.
Supervised Learning:Breast Cancer Detection analysis: Using logistic regression to predict the tummer is present or not.Using XGboost classifier model gives an 98% accuracy.
Supervised Learning:PIMA diabetes analysis: The datasets consist of several medical predictor (independent) variables and one target (dependent) variable, Outcome. Independent variables include the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. Using random forest algorithm we get the 81% accuray.
Air passenger analysis: To forcaste the value of Air passanger using the SARIMA algorithm.