Exploratory-Data-Analysis

Last Updated on May 3, 2021

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Exploratory Data Analysis or (EDA) is understanding the data sets by summarizing their main characteristics often plotting them visually. This step is very important especially when we arrive at modeling the data in order to apply Machine learning. Plotting in EDA consists of Histograms, Box plot, Scatter plot and many more. It often takes much time to explore the data. Through the process of EDA, we can ask to define the problem statement or definition on our data set which is very important. the above are some of the steps involved in Exploratory data analysis, these are some general steps that you must follow in order to perform EDA. There are many more yet to come but for now, this is more than enough idea as to how to perform a good EDA given any data sets.



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Regression Analysis On Wallmart Sales Data

Last Updated on May 3, 2021

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One of the leading retail stores in the US, Walmart, would like to predict the sales and demand accurately. There are certain events and holidays which impact sales on each day. There are sales data available for 45 stores of Walmart. The business is facing a challenge due to unforeseen demands and runs out of stock some times, due to the inappropriate machine learning algorithm. An 

ideal ML algorithm will predict demand accurately and ingest factors like economic conditions including CPI, Unemployment Index, etc.

 Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of all, which are the Super Bowl, Labour Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. Historical sales data for 45 Walmart stores located in different regions are available.

 Dataset Description

This is the historical data which covers sales from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales. Within this file you will find the following fields:

·        Store - the store number

·        Date - the week of sales

·        Weekly_Sales - sales for the given store

·        Holiday_Flag - whether the week is a special holiday week 1 – Holiday week 0 – Non-holiday week

·        Temperature - Temperature on the day of sale

·        Fuel_Price - Cost of fuel in the region

·        CPI – Prevailing consumer price index

·        Unemployment - Prevailing unemployment rate

 Holiday Events

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13

Labour Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13

Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13

Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

 Analysis Tasks

Basic Statistics tasks

1.     Which store has maximum sales

2.     Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation

3.     Which store/s has good quarterly growth rate in Q3’2012

4.     Some holidays have a negative impact on sales. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together

5.     Provide a monthly and semester view of sales in units and give insights

 Statistical Model

For Store 1 – Build prediction models to forecast demand

·        Linear Regression – Utilize variables like date and restructure dates as 1 for 5 Feb 2010 (starting from the earliest date in order). Hypothesize if CPI, unemployment, and fuel price have any impact on sales.

·        Change dates into days by creating new variable.

Select the model which gives best accuracy.

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House Price Predictor

Last Updated on May 3, 2021

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Here we have taken the information of a valid housing data set consisting of information of 500+ Houses. By taking all attributes as factors we will predict the price of the house. We are going to take advantage of all of the feature variables available to use and use it to analyze and predict house prices. Here we have to predict the price of the house on the basis of the following attributes:

~lot size – Square feet of the house I need. (Numerical)  

~Bedroom- How many bedrooms I need? (Numerical)

~bathroom – How many bathrooms I need? (Numerical)

~stories-How many stories building I need? (Numerical)

~driveway –Whether I need a driveway or not? (Binary)1 for yes and 0 for no.

~recreational room-Whether I need a rec room or not? (Binary)1 for yes and 0 for no.

~Gas hot water - Whether I need Gas Hot water or not? (Binary)1 for yes and 0 for no.

~full base- Whether I need a full base or not? (Binary)1 for yes and 0 for no.

~Air condition- Whether I need Air condition or not? (Binary)1 for yes and 0 for no.

By entering all these inputs of the attributes, and by using multivariate regression we will predict the house at price in $.

We have split the dataset into two parts training and testing set. Then by training the dataset we will use multivariate regression and predict the house of the price in the testing data set.

 

Here we have also compared actual and predicted price using Machine Learning  

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Ai Real Time Car And Pedestrian Tracking App

Last Updated on May 3, 2021

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AI REAL-TIME CAR AND PEDESTRIAN DETECTING APP USING PYTHON AND importing OpenCV

A real-time app using python as the programming language with importing open cv.

Learning from this:

  1. Haar features and algorithms
  2. how the haar cascade algorithm works in real-time upon grayscaled images
  3. why it works better on grayscaled images than taking colored frames instead.
  4. simple lines of code can do magic just putting the right things at right places

The result from this:

  1. we can detect images of person and vehicle and identify them in real-time webcam support to get the real time frame or taking the video as the import
  2. multiple real-time images can be detected and also with regular changing of dimensions
  3. this can lead to avoidance of the accident as also suggested by the tesla in their dashcam video


Challenges faced

  1. the most important challenge is to train the data and it's time-consuming so to build a simple prototype taking OpenCV trained data is beneficial as it saves lots of time.
  2. haar algorithm how it works is again one of the most important challenges as it has to be quite accurate to detect the face in real-time
  3. importing OpenCV required installation of multiple packages and different versions of python have different versions of that library.
  4. detecting person with nonliving vehicles is itself a challenge to make the training data in its work for both using two different cascade classifiers



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Machine Learning Algorithms

Last Updated on May 3, 2021

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I have created this projects by learning some machine learning algorithm's

Different algorithms I have learned are :

  1. K-means : In k-means algorithm I learned the working of algorithm using a dataset and loaded it. I have used sklearn and used metrics function in it to predict best fit score for dataset.
  2. KNN : In this algorithm I have used a car.data csv file in order to perform operations on it. I have trained the data and then labels of columns in order to fit the data in model. Then have predicted the acurracy of model.
  3. Regression : In this algorithm I have used different libraries such as numpy, pandas, sklearn and matplotlib for working on excel file. numpy and pandas is used for reading the csv file form directory and also to use series and dataframes of pandas. Matplotlib is used for graphical representation of model and sklearn is used to import its linear model for the data and train the data.
  4. SVM(Support Vector Machine Algorithm): In this algorithm I have used a different data set. Loaded that dataset into the algorithm with help of sklearn. works for classification and for regression, svm uses hyperplay to divide data in straights(line, 4D). Its a linear way to divide data.
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E-Commerce

Last Updated on May 3, 2021

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- Implement E-Commerce Web App which had started from 13 November to 12 December 2020.

- In this Web App user can able to purchase the various products which is available in Database and virtually placing the orders.

- Applied Python , DJANGO , Bootstrap and JavaScript ,especially focus on backend to explore the skill and knowledge of backend.

- This Web App consist of proper Database functionality which help to implement different function and operations.

- User can able to ask any query regarding products and processes , also there is special search functionality in which user can able to filter their required products by simply search on there.

- There are pop-down Cart which shows the product available in the Cart which is select by the user with two buttons in the bottom, one is Checkout and another is Clear Cart.

- On clicking the Checkout button it render the user to the place order page in which user should give all their details by filling the blanks input and finally place the Order.

- All the orders detail of user, orders and query will be stored in the Databases with their Username and Date.

- On clicking the Clear Cart button , it clear all the product which is select by the user for purchase.

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