Discover shraddhaghadage/Task2-Spark-Foundation Open Source project

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shraddhaghadage

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Text-Mining

For Text Mining assignment ONE 1) Perform sentimental analysis on the Elon musk tweets (Exlon musk.csv)

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Random_Forest_Company-data

A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A Random Forest can be built with target variable Sales (we will first convert it in categorical variable) & all

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Decision-tree-Fraud-Check

Use decision trees to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

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Text_Mining

For Text Mining assignment 1) Extract reviews of any product from ecommerce website like amazon 2) Perform emotion mining

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Decision-tree-Company-data

A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.

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KNN-k-nearest-neighbors-algorithm-

Prepare a model for glass classification using KNN for Glass dataset

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MediKit_Master_App

Python

shraddhaghadage.github.io

CSS

KNN-k-nearest_neighbors-algorithm-

Implement a KNN model to classify the animals in to categorie for Zoo dataset

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github-slideshow

A robot powered training repository 🤖

Ruby

Alexa___

Neural_Network_Gas-Turbine

predicting turbine energy yield (TEY) using ambient variables as features Gas Turbine

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Naive-Bayes

Prepare a classification model using Naive Bayes for salary data

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-Basic-Statistics_Level-1

Wallpaper_App_Using_Java

HTML

Basic-Statistics_Level-2-

Neural-Network

PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS

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Support-Vector-Machine-Salary-Data-

Prepare a classification model using SVM for salary data

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Logistic-Regression

Output variable -> y y -> Whether the client has subscribed a term deposit or not Binomial ("yes" or "no")

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Hypothesis__Testing

Sales of products in four different regions is tabulated for males and females. Find if male-female buyer rations are similar across regions.

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Support-Vector-Machine-Forest-Fires-

classify the Size_Categorie using SVM

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Clustering_

Perform Clustering(Hierarchical, Kmeans & DBSCAN) for the crime data and identify the number of clusters formed and draw inferences. Data Description: Murder -- Muder rates in different places of United States Assualt- Assualt rate in different places of United States UrbanPop - urban population in different places of United States Rape - Rape rate in different places of United States

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Simple_Linear-Regression

2) Salary_hike -> Build a prediction model for Salary_hike, Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.

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Hypothesis_Testing

A hospital wants to determine whether there is any difference in the average Turn Around Time (TAT) of reports of the laboratories on their preferred list. They collected a random sample and recorded TAT for reports of 4 laboratories. TAT is defined as sample collected to report dispatch. Analyze the data and determine whether there is any difference in average TAT among the different laboratories at 5% significance level.

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Hypothesis-Testing

A F&B manager wants to determine whether there is any significant difference in the diameter of the cutlet between two units. A randomly selected sample of cutlets was collected from both units and measured? Analyze the data and draw inferences at 5% significance level. Please state the assumptions and tests that you carried out to check validity of the assumptions.

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Random-Forest

Use Random Forest to prepare a model on fraud data treating those who have taxable_income <= 30000 as "Risky" and others are "Good"

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Association-Rules-

Prepare rules for the all the data sets 1) Try different values of support and confidence. Observe the change in number of rules for different support,confidence values 2) Change the minimum length in apriori algorithm 3) Visulize the obtained rules using different plots

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PCA

Perform Principal component analysis and perform clustering using first 3 principal component scores (both heirarchial and k mean clustering(scree plot or elbow curve) and obtain optimum number of clusters and check whether we have obtained same number of clusters with the original data (class column we have ignored at the begining who shows it has 3 clusters)df

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Multi-Linear-Regression

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in the past few years State -- states from which data is collected Profit -- profit of each state in the past few years

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Forecasting_CocaCola

Forecast the CocaCola prices and Airlines Passengers data set. Prepare a document for each model explaining how many dummy variables you have created and RMSE value for each model. Finally which model you will use for Forecasting.

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Forecasting_Airlines

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Multi_Linear_Regression

Consider only the below columns and prepare a prediction model for predicting Price.

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Recommendation-system

Problem statement. Build a recommender system by using cosine simillarties score.

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Simple-Linear-Regression-

1) Delivery_time -> Predict delivery time using sorting time --------------------------------------------------------- Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python.

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Hypothesis_Testing_

TeleCall uses 4 centers around the globe to process customer order forms. They audit a certain % of the customer order forms. Any error in order form renders it defective and has to be reworked before processing. The manager wants to check whether the defective % varies by centre. Please analyze the data at 5% significance level and help the manager draw appropriate inferences Minitab File: CustomerOrderForm.mtw

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Clustering

Perform clustering (hierarchical,K means clustering and DBSCAN) for the airlines data to obtain optimum number of clusters. Draw the inferences from the clusters obtained. Data Description: The file EastWestAirlinescontains information on passengers who belong to an airline’s frequent flier program. For each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. The goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers ID --Unique ID Balance--Number of miles eligible for award travel Qual_mile--Number of miles counted as qualifying for Topflight status cc1_miles -- Number of miles earned with freq. flyer credit card in the past 12 months: cc2_miles -- Number of miles earned with Rewards credit card in the past 12 months: cc3_miles -- Number of miles earned with Small Business credit card in the past 12 months: 1 = under 5,000 2 = 5,000 - 10,000 3 = 10,001 - 25,000 4 = 25,001 - 50,000 5 = over 50,000 Bonus_miles--Number of miles earned from non-flight bonus transactions in the past 12 months Bonus_trans--Number of non-flight bonus transactions in the past 12 months Flight_miles_12mo--Number of flight miles in the past 12 months Flight_trans_12--Number of flight transactions in the past 12 months Days_since_enrolled--Number of days since enrolled in flier program Award--whether that person had award flight (free flight) or not

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Calculator_Using_Java

Tic_Tac_Toe_Game

Python

Dialysis-of-Patients

NOTES_APP_

shraddhaghadage

Task1-Spark-Foundation

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Task3-Spark-Foundation

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Whatsapp_Saver_Master_App

Java

Task6-Spark-Foundation

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Task5-Spark-Foundation-

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Medicine_App_Reminder_Flutter

Secure_Weather_App

The weather app also provides atmospheric pressure, weather conditions, visibility distance, relative humidity, precipitation in different unites, dew point, wind speed and direction, in addition to ten days in future and hourly weather forecast.

Property-Insurance-Fraud-Detection

1) To check the scam made by people for claiming health insurance money. 2) In today's world as the world is evolving toward online there is increasing in number of frauds. 3) So some analyzation is required to detect this kind of fraud, for such big data. 4)Analyzing all this information individually or manually is tremendously difficult. 5)As such, automation of the process is required.

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Breast_Cancer_Classification_Project

Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of it can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of Breast Cancer and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex Breast Cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in Breast cancer pattern classification and forecast modelling. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. Because we are categorizing whether the tissue is cancerous or benign, we will train multiple Tree-based models for this procedure. We’ll experiment with hyper-parameters to see if we can enhance the accuracy. Try to solve the problem using the approach outlined below. For further information on each feature, consult the data dictionary. Decision trees (DTs) form the basis of ensemble algorithms in machine learning. These are powerful algorithms that can fit complex data. In this project, our focus is on understanding the core concepts of the Decision Tree for healthcare analysis, followed by understanding the different ensemble techniques.

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shraddhaghadage/Task2-Spark-Foundation

shraddhaghadage

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