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Machine Learning, Data Science, Data Analysis

Vasant Nagar, Karnataka, India
October 01, 2019

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Mir Muhammad Mujtaba


Bangaluru, India

A conscientious and multi skilled student aspiring to enter the world of duty, responsibility and professionalism. Curious about the way Machine Learning is utilized to process the data and used for creating the solutions for real life problems as well as visualizing the data by the techniques of Exploratory Data Analysis.


Advanced Certification course in Artificial

Intelligence and Machine Learning

IIIT - Hyderabad

B.Tech in Electronics and Communication

Islamic University of Science and Technology

Higher Secondary Part II (C.B.S.E)

Delhi Public School, Athwajan

Marticulation (J.K B.O.S.E)

Burn Hall School, Srinagar


Python for Data Science and Machine

Learning Bootcamp

- Udemy

Machine Learning A - Z

- Super Data Science

Machine Learning (Coursera)

Prof. Andrew N. G


Python Python for Data Science

Exploratory Data Analysis Machine Learning



Analysis of Indian GDP

Analyzing the GDP of different states and the areas of development on which the state-wise GDP depends.

Different states were divided into 4 groups C1, C2, C3 and C4 based on their G.D.P per capita. Observations were made regarding the biggest contributors to their GDP as well as recommendations based on the lowest contributors.


Analysis of Indian Trade Data

Data regarding the exports and imports of India is given. Imports have shown a cyclic trend for the mentioned duration whereas Exports have shown an increasing trend for the duration 2010-2014 and 2015-1018 seperately.

Exports and Imports were positively correlated for the duration 2015-2018 which is a positive sign for an economy. USA is the major contributor to Indian exports.

China is the major contributor to Indian imports.


House Prices: Regression Techniques

I created a Linear Regression model for predicting house prices based on various features affecting the cost of the house. The original number of input features were 81. Some of the features had to be dropped because of the null values and to avoid the curse of dimensionality .

The features were selected based on the conditions that percentage of null values be less than 40% and the correlation of the features w.r.t the Sale Price of the house. Mean absolute error was calculated to be 1.53.


Digit Recognizer

Digit Recognition system using kNN algorithm based on the pixel values of the image.

The image size is 784 pixels and all the pixels were used as the input features for the algorithm

No features could've been dropped since that would be dropping information.

Accuracy was calculated to be 96.8%


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