A central repo for code, sharing resources, workflows and roadmaps.Check the Projects section for project managemet plans.

0. Project proposal (In development)
1. Scrape data from all sites mentioned below. (In development)
2. Clean the data and remove unnecessary variables and noise. (In development)
3. Perfrom EDA and find out the relationships between variables. (Pending)
4. Build a model to predict/give an objective price of the house based on the parameters. (Pending)
5. Deploy the model using flask(Api), HTML, CSS, JS and PHP/Laravel(Backend). (Pending)
6. Documentation (Pending)

- major-project
  - csv_files
    - raw (Raw unclean data after scraping)
    - links (Links to be scrapped)
    - clean (Cleaned data)
- jupyter_notebooks (Notebooks to clean data and develop the model)
- scraping_files

• Hosing price prediction from towards data science
• Coursera linear regression Only good for conceptual/theoritcal learning. Uses outdated libraries.
• Vectorizaiton - Important for understanding numpy and pandas.
• Gradient descent - Important for understanding linear regression.
• Real estate prediction - For brief overview of how the project will work.
• First Class functions - How python's functions can be treated as objects.
• For Numpy, Pandas and Matplotlib
• Google's machine learning course
• Pip Python package manager
• Setting up python virtual env in VSCode
• Data Science Handbook
• Mathematics for machine learning Book
• Google Colab - Online GPU for extra power.

• creating and importing a python module and using its functions (files)
• namespace of functions in a python module
• how functions from other python modules are called
• Python data types: Lists, Tuples, dictionary and sets
• for loops, map function
• method call from a library vs function call ie how obj.method() works
• lambda function/closures/annonyomous function
• Python virtual environmets
• Basics of Object oriented python. Python class constructors. How a function gets access to the object it is called upon and how it manipulates the object.
• What is anacodna?
• How pip works

• Co-relation between variables and multicollinearity.
• Linear/Multiple/Logistic reression
• Squared error of regression line
• p-value, level of significance, null and alternate hypothesis.
• contour plot
• Fit a line using least square method.
• Vectors, Partial derivative, gradient of a function and gradient descent.

• Scatter plot
• Heatmap - To find multicollinearity
• Box plot
• Histograms
• Violin plot

• One hot encoding
• Dummy variable trap
• Co-relation does not mean causation

1. Ridge regression - Because the data seems to have multicollinearity
2. Linear regression - Related = Wald's test
3. Logistic regression ??
4. Convolution neural networks
5. Random forest
6. ID3

Use waka as a testing tool and may use external librariess to check efficiency of the model.

• Using VSCode commmand pallete
• The Spyder IDE - Optional for now
• Markdown - Very important for editing and adding resourses.
• Land measurement - Details about the different units of measurement of land.
• Creating checkmarks in projects
• How Much Training Data is Required for Machine Learning? (Article)
• Install jupyter lab using conda for notebook dark theme:

Go through the basic contents of the docs atleast once

• Numpy (Vectorization and broadcasting in Numpy)
• Pandas
• Seaborn
• Matplotlib

1. Price
2. Location in District
3. Number of rooms
4. Number of floors
5. Area
6. Time posted
7. Road size and road type*
8. Room size *
9. Bedrooms *
10. Bathrooms *
11. Car parking *
12. kitchen *
13. Living room *
14. Garage
15. Furnished ? *
16. Guestroom *

• Files/Modules and variables = snake_case
• directories = all lowercase preferably without underscores
• classes = PascalCase