sreehariE's Stars
sreehariE/sreeharie.github.io
Sree Hari Personal Website
reddyprasade/Zero-to-Hero-with-Python-2.x
Learn Python For any one and any Where but i need you time to learn
reddyprasade/io19
reddyprasade/Face--Recognition-with-Opencv
Image recognition using python Very Easy Write and to Learn
reddyprasade/Bicycle-sharing-system-in-US
A bicycle-sharing system, public bicycle system, or bike-share scheme, is a service in which bicycles are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" and return it at another dock belonging to the same system. Docks are special bike racks that lock the bike, and only release it by computer control. The user enters payment information, and the computer unlocks a bike. The user returns the bike by placing it in the dock, which locks it in place. Other systems are dockless. For many systems, smartphone mapping apps show nearby available bikes and open docks.
reddyprasade/Machine-Learning-With-R
Machine Learning in R
reddyprasade/An-Introduction-to-Statistical-Learning-with-Applications-in-R
An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science.
reddyprasade/Pandas-with-Python
Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.
reddyprasade/Digit-Recognizer-with-Python
We used preprocessing programs made available by NIST to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensional and gives in-variance to small distortions.
reddyprasade/Uber-Data-Analysis-Project
Data is the oil for uber. With data analysis tools and great insights, Uber improve its decisions, marketing strategy, promotional offers and predictive analytics. With more than 15 million rides per day across 600 cities in 65 countries, Uber is growing rapidly with Data Science starting from data visualization and gaining insights that help them to craft better decisions. Data Science tools play a key role in every operation of Uber.
reddyprasade/SOCKETS-WITH-PYTHON-
Socket Programming with Python
reddyprasade/Networking-With-Python-
Client-Server-Programing
reddyprasade/Regression-on-fuel-consumption-in-canada
Datasets provide model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada.
reddyprasade/Support-Vector_Machine-For-Cat-Dog-Image-Classification
Support Vector Machine For Image Classification
reddyprasade/ML-With_python_pract
reddyprasade/Data-Science-With-R
The goal of “R for Data Science” is to help you learn the most important tools in R that will allow you to do data science.
reddyprasade/Introducation-to-julia-Program
Julia is a high-level, high-performance, dynamic programming language. While it is a general purpose language and can be used to write any application, many of its features are well-suited for high-performance numerical analysis and computational science.
reddyprasade/Increasing-Subscription-Rate
Developing machine learning model to predict a user who is most unlikely to subscribe for the paid membership of the app, used Logistic Regression to classify the users based on the app behavior usage and was able to predict with an accuracy of 77%, Overall this can be helpful for marketing team to target the ads for the user who are less likely to subscribe for paid version, this also helps to give the promotional offers only to specific set of customers there by reducing the marketing cost.
reddyprasade/Top-Zomato-Restaurants-in-Bengaluru
The basic idea of analysing the Zomato dataset is to get a fair idea about the factors affecting the aggregate rating of each restaurant, establishment of different types of restaurant at different places, Bengaluru being one such city has more than 12,000 restaurants with restaurants serving dishes from all over the world. With each day new restaurants opening the industry hasn’t been saturated yet and the demand is increasing day by day. In spite of increasing demand it however has become difficult for new restaurants to compete with established restaurants. Most of them serving the same food. Bengaluru being an IT capital of India. Most of the people here are dependent mainly on the restaurant food as they don't have time to cook for themselves. With such an overwhelming demand of restaurants it has therefore become important to study the demography of a location. Hence build a model to predict the rating of the each restaurants.
reddyprasade/Python-Pattern-Programming
Pattern programming is one type of programming process it consists of both static and dynamic
reddyprasade/Automate-the-loan-eligibility-process-real-time-based-on-customer
Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers. Here they have provided a partial data set.
reddyprasade/Converting-Image-to-Array-Image-Processig-by-Using-Scikit-image-
scikit-image is a Python package dedicated to image processing, and using natively NumPy arrays as image objects. This chapter describes how to use scikit-image on various image processing tasks, and insists on the link with other scientific Python modules such as NumPy and SciPy.
reddyprasade/Unstructured-Images-to-Structure-Array-in-Python
Machine learning, Python uses image data in the form of a NumPy array, i.e., [Height, Width, Channel] format. To enhance the performance of the predictive model, we must know how to load and manipulate images. In Python, we can perform one task in different ways. We have options from Numpy to Pytorch and CUDA, depending on the complexity of the problem. By the end of this tutorial, you will have hands-on experience with: Loading and displaying an image using Matplotlib, OpenCV and Keras API Converting the loaded images to the NumPy array and back Conducting basic manipulation of an image using the Pillow and NumPy libraries and saving it to your local system. Reading images as arrays in Keras API and OpenCV
reddyprasade/Telecommunications-Data-for-Predicting-Customer-Churn
We’ll use a telecommunications data for predicting customer churn. This is a historical customer data where each row represents one customer. The data is relatively easy to understand, and you may uncover insights you can use immediately. Typically it’s less expensive to keep customers than acquire new ones, so the focus of this analysis is to predict the customers who will stay with the company. This data set provides info to help you predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs. The data set includes information about: Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents
reddyprasade/Carbon-Dioxide-Emissions-Predication-of-FuelConsumption-Data-Sets
which contains model-specific fuel consumption ratings and estimated carbon dioxide emissions for new light-duty vehicles for retail sale in Canada
reddyprasade/Learn_Pytorch
PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is primarily developed by Facebook's artificial intelligence research group. It is free and open-source software released under the Modified BSD license.
reddyprasade/Theano-Practices
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.ndarray in Theano-compiled functions. transparent use of a GPU – Perform data-intensive computations much faster than on a CPU. efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs. speed and stability optimizations – Get the right answer for log(1+x) even when x is really tiny. dynamic C code generation – Evaluate expressions faster. extensive unit-testing and self-verification – Detect and diagnose many types of errors. Theano has been powering large-scale computationally intensive scientific investigations since 2007. But it is also approachable enough to be used in the classroom (University of Montreal’s deep learning/machine learning classes).
reddyprasade/DataSet-for-ML-and-Data-Science
Freely Available Data Sets For Real world Problems
reddyprasade/Pandas-Practice
Pandas
reddyprasade/Time_Series_Analysis
Time Series Analysis with Python numpy pandas