Making Our 1st Kaggle Submission

rushikesh jachak (~rushikesh)


The tutorial will include Exploratory Data Analysis, followed by ML models and improvising them to boost your rank in Our Kaggle Submission (House Prediction). By the end of tutorial, audience will be able to implement Practical Machine Learning Methods and apply them on any real world Datasets.

Outline of Tutorial :-

(NOTE : All of the mentioned topics will be conducted in Python in Anaconda Environment or on Google Colab)

1.Exploratory Data Analysis (45 Minutes) :

A. Handling Missing Data,

B. Dummy Variables,

C. Data Visualisation,

D. Feature Selection and Extraction.

2.Linear Regression Model from Scratch (45 Minutes)

A. Implementing Cost Function

B. Gradient Descent

C. Training Validation Testing Split on Data Set.

D. Tuning Hyperparameters

E. Submitting the Predictions

3. Regularisation to Improve Results (20 Minutes)

4. Other ML Techniques such as Random Forest using sklearn library to compare models.(20 Minutes)



  • Basics of Python and Programming Intuition.

  • Numpy

  • Pandas

Good to Know :

  • Sklearn,Regression

Content URLs:

The Link Contains Jupyter Notebook for both Exploratory Data Analysis as well as ML Models

Speaker Info:

As a student I have faced several challenges in learning and implementing machine learning models that could be applied on real world datasets. And to Share this knowledge we have founded Analytics and Insights Club where we make people learn how to implement ML models in Real world. I am also The Dean of School of AI, Aurangabad where i along with my team takes sessions on Practical Machine Learning.

We have a past Experience of Speakers at Scipy International Conference, FOSSEE 2018 at IIT-Bombay where we conducted a workshop on Apache Spark and Its Implementation on Real World Datasets. Along with it, we also delivered a talk on PyDICOM - A Medical Imaging Library in Python.

As a love and curiosity towards research, we are currently working on Segregation of Solid Waste Using Artificial Intelligence and Have presented our research work at ICACCP, 2019 which is going to be published in IEEE Xplore.

We have Experience of more than 5 Hackathons, where our team has been into finale for all of the 5. Winners of Smart India Hackathon 2019 and AIR 6 at Smart India Hackathon 2018. We have been awarded with Most Innovative Solution Award by Deloitte at SIH 2018 and IOT based Smart Waste Management System by Cognizant at SIH 2019.

Currently Incubated under Ankur For Solving Problem of Solid Waste at Aurangabad.

Speaker Links:

Rushikesh Jachak :

Purva Chaudhari :

Id: 1268
Section: Data Science, Machine Learning and AI
Type: Workshop
Target Audience: Intermediate
Last Updated: