Supervised Learning - Prediction

     English Dictionary App and Library Book Management System development is the main aim of this project English Dictionary App      English dictionary app is a collection of all english words along with their meaning. The aim of this project is to develop an english dictionary app using…

User Behavior Study for a Social E-Commerce Platform        Customer to Customer model is growing fast with e-commerce platforms where sellers may be required to pay some amount and buyer can buy it without paying anything. E-Commerce website brings the seller and buyer to the same platform. Analyzing…

Project 2Using the existing datasets of red wine and white wine data, we can understand the relationships between the features and determine the quality rating of the wine based on these features.1. Displaying the data of white wine and red wine2. Size of both white wine and red wine datasets3. The number of null…

Using the existing dataset of medical insurance data, we can understand the relationships between the features and the target variable and make predictions on new data.

Introduction      Humans are a brilliant natural system which is having the great capability of viewing and observing multiple images in a short span of time, then consume and deduce from them without even realizing that how the actual process is completed. But this case is not at all similar with machines…

Implementing Clustering and Predicting the Class of Car from "Car Dataset" Importing libraries import pandas as pd import numpy as np import seaborn as sn from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LinearRegression from sklearn.ensemble…

Data Cleaning on Automobile 1985 Dataset No. of data points: 205 Number of Attributes: 26 total -- 15 continuous -- 1 integer -- 10 nominal Attribute Information: symboling: -3, -2, -1, 0, 1, 2, 3 normalized-losses: continuous from 65 to 256 make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz,…

Unsupervised Learning  Kmeans   1. Calculating Similarity id Data is Categorical in Nature        The simplest way to find similarity between two categorical attributes is to assign a similarity of 1 if the values are identical and similarity of 0 if the values are not identical.…

Supervised Learning Classification   1. Neural Network     Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking…

Supervised Learning Classification   1. KNN on Surface Defects in Stainless Steel Plates K-Nearest Neighbors is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. It classifies new cases based on feature similarity measures. KNN algorithm K in the KNN algorithm…

Supervised Learning Classification   1. Pros and Cons of SVM Support Vector Machine (SVM) “Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. However,  it is mostly used in classification problems. In the…

Supervised Learning  Prediction   1. Gradient Descent  The gradient descent in python using mean square error as loss function on the following data set: x = [0,1,2,3,4] y = [1,2,3,4,5]   Theory: `J = (1/n) sum(y(i) - (mx(i) + b))^2` x(i) are the input m and b are the weights  mx(i)+b is the predicted…

Basics of ML & AL   1. Business Moments:   2. Significance of expected value when simple mean (Sum of all observations/number of observations) is already in place:       It is true that simple mean is sufficient enough for having an idea about the data by computing with it's formula (Sum…

Basics of Probability and Statistics 1. Sample Variance vs Population Variance The formula to calculate population variance is: `sigma ^2 = (sum (x_i - mu_x ) ^2)/N` The formula to calculate sample variance is: `s^2 = (sum (x_i - mu_x ) ^2)/(N-1)` The difference between the two formulas is: When we calculate population…

     Data Processing for an international humanitarian NGO to classify the countries using socio-economic and health factors that determines the overall development of a country and to also suggest the countries that have to given utmost importance.   Methodology: This analysis and prediction can be…

      From the given clinical parameters about a patient, prediction models like Logistic Regression, K-Nearest Neighbours Classifier, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier to predict whether or not the patient has heart disease.   Methodology High accuracy prediction…

     Exploratory Data Analysis  is applied to investigate the data and summarize the key insights. This is done to provide basic understanding of the data. EDA for the given dataset can be done by following the steps below 1. Adding the column names to the dataset 2. Checking and Cleaning Data Quality:…