This repository contain 5 projects
1.Iris_Data_Analysis
2.Black Friday
3.Urban_Sound_Classification projects.
4.Daily_Minimum_Temperature
5.Air_Passengers
(classification problems using Basic ML algorithm) 1.Iris Data Classification: Given the numeric parameter which the dataset contain are Sepal width, Sepal length, Petal width, Petal length. Predict the classes based on these parameter possible classes are Versicolor, Setosa and Virginica.In this problem I used some of the basic classification algorithm to classify this dataset.
(Regression Problem using ensemble approach)
2.Black Friday: The data set contains customer
demographics (age, gender, marital status, city_type,
stay_in_current_city), product details (product_id and product
category) and Total purchase_amount from last month. Created a model to predict the purchase amount. In this problem I explain how to apply ensemble approach to solve regression problem.
(classififcation problems using deep learning)
3.Urban sound Classification: From datasets consists of
8000+ sound from 10 classes predict the class label in which
it belongs to.In this problem I explain how to extract features from audio sample and Apply Build ANN with keras, tensorflow and solve classification problem.
(Time series Forcasting)
a.Time series analysis using (AR): Time series analysis of minimum temperature from dataset contain about 3500+ sample of timeseries minimum temperature data using Autoregression model.
b. Time Series analysis using (ARIMA): Time series analysis of number of Air Passengers travelling next month by using previous month data by using ARIMA model.
Details of the process is mentioned in each IPython notebook and data is present inside respective folder except Urban_Sound_Classification projects you will find the data in https://drive.google.com/drive/folders/0By0bAi7hOBAFUHVXd1JCN3MwTEU because of large volume.If any help assist is required you can directly mail me at [email protected], I am happy to answer.
