Ipl Score Prediction

Last Updated on May 3, 2021


I've created this model recently using machine learning.

I want to upload to heroku app but am facing some error, that's why i can't.

So basically this model is trained to predict the score of any IPL team using some information from user like :-

  1. Select a Batting Team
  2. Select a Bowling Team
  3. Overs
  4. Wickets
  5. Runs scored in previous 5 overs
  6. Wicket falls in previous 5 overs and many more.

I've used the following things for successful implementation of the project::-

  1. Flask
  2. gunicorn
  3. itsdangerous
  4. Jinja2
  5. MarkupSafe
  6. Werkzeug
  7. numpy
  8. scipy
  9. scikit-learn
  10. matplotlib
  11. pandas

This model is 98% accurate but sometimes miracle happen in some match, which it can't predict (:X).

More Details: IPL score prediction

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Black Friday Sales Prediction

Last Updated on May 3, 2021


Black Friday Sales Prediction is simply a prediction of sales of different products. Main goal of this project is to find out customer purchase behaviour against various products of different categories. I have  purchase summary of various customers for selected high volume products from last month. The data set also 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. Based on this data we will predict sales.

For simplicity i divided my projects into small parts-

  1. Data Collection :- I collected data from 'Anylitical Vidhya' as a CSV file. We have two CSV file one is train data which is used for training the data and other is test data which is used for prediction based on training of model.
  2. Import Libraries:- I import differnt sklearn package for algorithm and different tasks.
  3. Reading data:- i read the data using pandas 'read csv()' function.
  4. Data Preprocessing -: In this part i first found missing values then i remove a column or imputed some value (mean,mode,median) According to the amount of data missing for a particular column.

I checked the unique value in each column. Then i did label encoding to convert all string types data to integer value. I find out correlation matrix which shows the correlation between columns to each other.

Then i split the data. Then i create a regression model. I trained that regression model using Random Forest Algorithm .I feed training dataset to model using random forest algorithm. After creating model i did similiar data preprocessing to test dataset . And then i feed test dataset to trained regression model which predict the values of this test dataset. And then i found accuracy of this model using actual target value which is given in training dataset. and predict target value which we predict from test dataset.

More Details: Black Friday Sales Prediction

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Last Updated on May 3, 2021


Sense+ makes the approach to helping those in need proactive compared to the traditional reactive approach. It utilises speech, facial recognition and other technologies to infer emotions of users.


The global pandemic has revealed the growing issue and importance of mental health, in particular one’s accessibility to mental health services and the detection of someone suffering from stress, anxiety or other mental health conditions.

We personally have seen that being mentally well allows us ability to work and study productively.

It is the on going issue of those mentally unwell not approaching anyone due to societal stigma of seeking treatment that worries us.

Our project/proof of concept aims to make the change the approach of helping those in need proactive, rather than waiting for individuals to come forward by themselves, all whilst aiding to reducing the stigma associated with suffering from mental health issues

What it does

Our program integrates voice and facial recognition to detect/infer an individual’s emotions.

The voice using sentiment analysis to detect keywords from an audio transcript. These keywords are categorised as neutral, positive or negative. Natural language processing and regular expressions are utilised to break down audio transcripts into multiple sentences/segments.

The facial recognition uses convolutional neural networks to pick up features of ones faces, to identify emotions. Videos broken down into multiple frames which are fed into neutral network to make the predication.

This model is trained and validated using Facial Expression Recognition data from Kaggle (2013).

As of now we have nearly turned the above concept into an app which allows users to upload multiple videos, which are then analysed and results/predictions are returned about the emotional state of an individual.

The implications of this is that it can aid in indicating whether the user should seek professional help, or at the very least make them possibly aware of their current mental state.

How we built it

The frontend was developed using Java (Android Studio), whilst our backend was developed in Python, with the help of python packages such as TensorFlow, Keras and speech recognition. The frontend and backend communicate through Amazon AWS platform. AWS lambda is utilised so our code can be ran serverless and asynchronously. S3 is employed as a bucket to upload videos from the frontend so the backend process them. Additionally, output from the backend is stored as JSON in S3 so the frontend can retrieve for display purposes.

Challenges we ran into

The main challenge we faced was learning how to make our frontend and backend communicate. With the help of mentors, from Telstra, Atlassian and Australia Post they provided us insights into solving our main issue. Though we did not quite get everything integrate into a single working piece of software.

Learning aspects of AWS was also challenging considering no one on our team had any prior experience.

On top of that applying TensorFlow and Keras in a full project context was challenging in terms of the lack of resources (hardware) and training data was a timely process.

Accomplishments that we're proud of

Despite not completing a functioning prototype at this point in time, we are proud that we delved into new software, tools and packages that we never had prior experience with and tried our best to utilise them. Finally, we are proud of how we conducted ourselves as a team, given the diverse nature and range and variation of skills and knowledge.

What we learned

First of all, the importance of communicating as a team is crucial. Main points include team ideation, being critical and delegating appropriately according to each team members strengths. Another point is learning to approach mentors or team members when you are struggling. Overcoming the stigma or anxiety of admitting being ‘lost’ is important lesson, and we found when we overcame these barriers, we were able to progress.

What's next for Sense+

At the moment the Sense+ remains at its core an idea, not necessarily a piece of deliverable software. In the future we seek to improve upon accuracy when analysing and detecting emotion. This includes but isn’t limited to; more sophisticated sentiment analysis, improving the modelling and taking advantage of other bio-metrics that may come with the advanced of technology such as detecting heartbeat etc.

In terms of reach and usage, possibly uses is that companies could employ such software to monitor the well-being of employees. In the future the software could be more passive so that individuals can be monitored (of course with consent and confidential) in a more natural manner. This would yield accurate information on employee well-being rather than self-reports where people may lie because of stigma and fear. This could greatly boost the overall productivity and mental well-being within the company.

Other sectors this could be applied in is hospitals and education.

More Details: Sense+

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Retail Analysis Of Walmart Data

Last Updated on May 3, 2021



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

  • Which store has maximum sales
  • Which store has maximum standard deviation i.e., the sales vary a lot. Also, find out the coefficient of mean to standard deviation
  • Which store/s has good quarterly growth rate in Q3’2012
  • 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
  • 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.

More Details: Retail Analysis Of Walmart Data

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Snake Game

Last Updated on May 3, 2021


The objective of this project is to build a snake game project using Python. In this python project, the player has to move a snake so it touches the red dot . If the snake touches itself or the border of the game then the game will over.

The following are the methods I used :

Turtle module, random module, time module, and concept of python by basically used in this project

Turtle module gives us a feature to draw on a drawing board

Random module will be used to generate random numbers

Time module is an inbuilt module in python. It provides the functionality of time.

The steps to build a snake game project in python:

The first is Importing libraries then we move to create a game screen and also the creation of snake and red dot. Keyboard binding is to be done next followed by the game main loop.

We require turtle, random, and time module to import

To create Game screen I used :-

  • title() :  will set the desired title of the screen
  • setup() : used to set the height and width of the screen
  • tracer(0) : will turn off the screen update
  • bgcolor()  : will set the background color
  • forward()  : will use to move the turtle in a forwarding direction for a specified amount
  • right() : used to turn the turtle clockwise and left() used to turn the turtle anticlockwise
  • penup() : will not draw while its move

For creating Game screen I used :-

  • Turtle() :  will be used to create a new turtle object
  • hideturtle() :  will use to hide the turtle
  • goto() :  used to move the turtle at x and y coordinates

Now by adding key binding in which the directions in which the snake will go and how will be decided.

If the snake touches the border of the game then the game will over. screen.clear() will delete all the drawing of the turtle on the screen

We successfully developed Snake game project in python.

More Details: Snake Game

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Design And Analysis Of Automobile Chasis

Last Updated on May 3, 2021


Completed under the guidance of Dr. Shailesh Ganpule, Department of Mechanical and Industrial Engineering during August 2019 to November 2019. The objective of this design analysis is to find out the best material and most suitable cross-section for a common “Goods Carrier Truck” ladder chassis with the constraints of maximum shear stress, equivalent stress and deflection of the chassis under maximum load condition. In present the Ladder chassis which are used for making buses and trucks are C and I cross section type, but here we also analysed the Box type and Tube Type. In Trucks generally heavy amounts of loads are carried due to which there are always possibilities of being failure/fracture in the chassis/frame. Therefore Chassis with high strength cross section is needed to minimize the failures including factor of safety in design. The different vehicle chassis have been modeled by considering three different cross-sections namely C, I , Rectangular Box (Hollow) and Tubular type cross sections. The problem to be dealt with for this dissertation work is to Design and Analyze using suitable CAD software and Ansys 19.2  for ladder chassis. The report is the work performed towards the optimization of the Truck chassis with constraints of stiffness and strength. The modeling is done using Solid works, and analysis is done using Ansys 19.2 .. The overhangs of the chassis are calculated for the stresses and deflections analytically are compared with the results obtained with the analysis software. Involved in designing of Heavy Loaded Vehicle chassis in SolidWorks with stress simulation and strain analysis in Ansys. Carried out Failure Analysis using Von Mises Criterion to obtain their sustainability. Performed Convergence Analysis to select the most optimized model with the desired factor of safety. Compared software(practical) value obtained with theoretical value obtained.


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