Jasjit Singh Dhanoa's starred repositories
Hotel_Revenue_project
End to End Data Analytics Project
TMDb-API-Project
The clear, simple syntax of Python makes it an ideal language to interact with REST APIs, and in typical Python fashion, there’s a library made specifically to provide that functionality: Requests. Python Requests is a powerful tool that provides the simple elegance of Python to make HTTP requests to any API in the world. At Nylas, we built our REST APIs for email, calendar, and contacts on Python, and we process over 500 million API requests a day, so naturally, we depend a ton on the Python Requests library.
Support-Vector-Machine-and-Means-Clustering-
SVM and k-means are very different. SVM is supervised (supervised classification) and k-means is unsupervised (clustering). so it depend on the goal of your application. for supervised classification, SVM is the best algorithm and you need to precise je most efficient kernel (linear, RBF, etc...)
Employee-Database-Project
Python SQLite3 module is used to integrate the SQLite database with Python. It is a standardized Python DBI API 2.0 and provides a straightforward and simple-to-use interface for interacting with SQLite databases. There is no need to install this module separately as it comes along with Python after the 2.5x
Startup-Funding-Project
Exploratory Data Analysis (EDA) is an approach to analyze the data using visual techniques. It is used to discover trends, patterns, or to check assumptions with the help of statistical summary and graphical representations.
Decision-Tree-and-Random-Forest
A decision tree combines some decisions, whereas a random forest combines several decision trees. Thus, it is a long process, yet slow. Whereas, a decision tree is fast and operates easily on large data sets, especially the linear one. The random forest model needs rigorous training.
K-Nearest-Neighbors
K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
Logistic-Regression
Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications. Logistic Regression is used when the dependent variable(target) is categorical. For example, To predict whether an email is spam (1) or (0) Whether the tumor is malignant (1) or not (0) Consider a scenario where we need to classify whether an email is spam or not. If we use linear regression for this problem, there is a need for setting up a threshold based on which classification can be done. Say if the actual class is malignant, predicted continuous value 0.4 and the threshold value is 0.5, the data point will be classified as not malignant which can lead to serious consequence in real time. From this example, it can be inferred that linear regression is not suitable for classification problem. Linear regression is unbounded, and this brings logistic regression into picture. Their value strictly ranges from 0 to 1.
Linear-Regression
Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable's value is called the independent variable.
Finance-Data-Project
Finance Data Project In this data project we will focus on exploratory data analysis of stock prices. Keep in mind, this project is just meant to practice your visualization and pandas skills, it is not meant to be a robust financial analysis or be taken as financial advice
911-calls-Project
For this capstone project we will be analyzing some 911 call data from Kaggle. The data contains the following fields
Visualisation_tutorial_for_Machine_learning
Matplotlib is one of the most popular and oldest plotting libraries in Python which is used in Machine Learning. In Machine learning, it helps to understand the huge amount of data through different visualisations.
Feature-Selection-Method
While developing the machine learning model, only a few variables in the dataset are useful for building the model, and the rest features are either redundant or irrelevant. If we input the dataset with all these redundant and irrelevant features, it may negatively impact and reduce the overall performance and accuracy of the model. Hence it is very important to identify and select the most appropriate features from the data and remove the irrelevant or less important features, which is done with the help of feature selection in machine learning.
Python-Feature-Engineering-Project-
This dataset has funding information of the Indian startups from January 2015 to August 2017.
Data_Structures-
A data structure is a method of organizing data in a virtual system. Think of sequences of numbers, or tables of data: these are both well-defined data structures. An algorithm is a sequence of steps executed by a computer that takes an input and transforms it into a target output. Together, data structures and algorithms combine and allow programmers to build whatever computer programs they’d like. Deep study into data structures and algorithms ensures well-optimized and efficient code.
Pandas_Tutorial_for_Machine_learning
Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. As one of the most popular data wrangling packages, Pandas works well with many other data science modules inside the Python ecosystem, and is typically included in every Python distribution, from those that come with your operating system to commercial vendor distributions like ActiveState’s ActivePython.
Numpy_tutorial_for_Machine_learning
NumPy is a very popular python library for large multi-dimensional array and matrix processing, with the help of a large collection of high-level mathematical functions. It is very useful for fundamental scientific computations in Machine Learning.
Pentesting-Bugbounty
Bringing infosec community, group and leaders together that solve community challenges, problems, create cultural and provide value to Infosec community.
juice-shop
OWASP Juice Shop: Probably the most modern and sophisticated insecure web application
awesome-ctf
A curated list of CTF frameworks, libraries, resources and softwares
bounty-targets-data
This repo contains hourly-updated data dumps of bug bounty platform scopes (like Hackerone/Bugcrowd/etc) that are eligible for reports