manikantareddychamala / mani.portfolio

Data scientist (Portofolio)

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Technical skills :

python , clanguage, Data structures , SQLData base , operating system

EDUCATION:

B.Tech - 8.2/10.0 Year: 2020 - 2024

10th class - 9.7/10.0 Year: 2018

12th class - 8.5/10 Year: 2020

-🔭 I’m currently working on predecting real estate prices using linear regression and support vector machine in machine learning

• 🌱 I’m currently learning Machine learning in Data science

• 👨‍💻 All of my projects are available at https://github.com/manikantareddychamala/New-one

• 📫 How to reach me [email protected] and [email protected]

*projects:

1.Real estate prices using linear regression and support vector machine in machine learning

Linear Regression:

Linear regression is a simple yet powerful technique used for predicting a continuous variable based on one or more independent variables. In the context of real estate prices, you could use linear regression to predict the price of a house based on features like square footage, number of bedrooms, number of bathrooms, location, etc.

Support Vector Machines (SVMs):

SVMs are a type of supervised learning algorithm that can be used for both classification and regression tasks. In regression, SVM tries to find a hyperplane that best separates the data into different classes while maximizing the margin (distance) between the hyperplane and the data points. In real estate, SVM regression can be used similarly to linear regression to predict house prices based on various features.

-🔭 project Github is:- predecting real estate prices using linear regression and support vector machine in machine learning

2.Movie recommended system in Datascience using machine learning:

Collaborative Filtering:

This approach recommends movies based on the preferences of similar users or items. You can use techniques like user-based collaborative filtering or item-based collaborative filtering.

Content-Based Filtering:

This approach recommends movies based on the similarity between the attributes of movies and the user's preferences. Features like movie genres, actors, directors, etc., are used to

compute similarity scores.

Hybrid Methods: Combine collaborative filtering and content-based filtering to improve recommendation quality.

source code:

https://colab.research.google.com/github/manikantareddychamala/MOVIE-RECOMMENDER-SYSTEM-IN-DATA-SCIENCE-ML-/blob/main/Recommender%20Systems%20with%20Python.ipynb

-🔭 Project Github is:- https://github.com/manikantareddychamala/MOVIE-RECOMMENDER-SYSTEM-IN-DATA-SCIENCE-ML-

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