bushra-ansari,Bushra Ansari,github

breast-cancer-prediction-by-ensemble-techniques

Given the details of cell nuclei taken from breast mass, objective is to predict whether or not a patient has breast cancer using the Ensembling Techniques. This is a classification problem. The dataset consists of several predictor variables and one target variable, Diagnosis. The target variable has values 'Benign' and 'Malignant', where 'Benign' means that the cells are not harmful or there is no cancer and 'Malignant' means that the patient has cancer and the cells have a harmful effect.

computervision-handsign_detection

American Sign Language (ASL) is a complete, natural language that has the same linguistic properties as spoken languages, with grammar that differs from English. ASL is expressed by movements of the hands and face. It is the primary language of many North Americans who are deaf and hard of hearing and is used by some hearing people as well. This Computer Vision project is done by OpenCV Python Package.

computervision-licence_plate_detection

This project has been done by using OpenCV package in Python. The objective is to train the computer to read the license plates whenever the computer camera encounters a vehicle and store the pictures of only the license plate to keep a track of all the vehicles. It involves the concept of Image Recognition.

diabetes-prediction-by-decision-tree-algorithm

Decision Tree is a supervised learning algorithm. The given problem is a classification problem. The data set consists of various predictors and a target variable - Outcome. Objective is to predict whether a person is diabetic or non-diabetic.

e-commerce-customer-segmentation-by-kmeans-clustering

Given the e-commerce data, k-means clustering algorithm is used to cluster customers with similar interest. The data was collected from a well known e-commerce website over a period of time based on the customer’s search profile.

forecasting-sales-of-a-furniture-store

One of the most important tasks for any retail store company is to analyze the performance of its stores. The main challenge faced by any retail store is predicting in advance the sales and inventory required at each store to avoid overstocking and under-stocking. This helps the business to provide the best customer experience and avoid getting into losses, thus ensuring the store is sustainable for operation.

market-positioning-of-mobile-by-k-nearest-neighbors-classification

This project is based a classification algorithm i.e. KNN which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobiles phones are classified in accordance with their price range.

naive-bayes-classifier

This project is based on a classification algorithm i.e. Naive Bayes which is run on a mobile dataset consisting of 2000 rows and 15 columns. It is a multi-class problem where mobile phones are classified in accordance with their price range. There are four classes of price ranging from 0 to 3, 0 indicating cheaper mobiles phones and 3 representing expensive mobile phones. Univariate analysis is conducted to understand individual predictors and bivariate analysis is conducted to infer relationship between predictors with other predictors and target variable. Important features are identified by Random Forest.

predicting-term-deposit-subscription-by-a-client-by-svm-classifier

Support Vector Machine Classification model is applied on bank dataset containing 41188 rows and 21 columns. The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to assess if the product (bank term deposit) would be ('yes') or not ('no') subscribed.

property-price-prediction-by-linear-regression

This project is based on House Prices dataset which consists of 2073 rows and 81 columns. Objective is to predict the price of the houses by a Regression Technique called Linear Regression after performing Principal Component Analysis.

taxi-fare-prediction-by-random-forest-regressor

This project is based on a supervised learning algorithm called Random Forest Regressor. The dataset consists of approximately one lakh rows and seven columns. Target variable is fare_amount.

travel-review-segmentation-by-hierarchical-clustering-

Given the google rating data, hierarchical clustering algorithm is used to cluster reviews. This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and the average user rating per category is calculated.

vaccine-usage-prediction-by-logistic-regression

Logistic Regression is a supervised learning algorithm. This project is based on a classification problem where the objective is to predict how likely it is that the people will take an H1N1 flu vaccine. The dataset consists of 34 predictors including target variable i.e. h1n1 vaccine.

webscrapping-sentimentanalysis

In this project, 500 tweets from various news channel have been extracted from twitter and their sentiment analysis is done with the help of polarity score. Also, 2 sports news have been extracted from Time of India website along with captions, headlines and photos.

bushra-ansari Goto Github PK

Bushra Ansari's Projects

breast-cancer-prediction-by-ensemble-techniques

computervision-handsign_detection

computervision-licence_plate_detection

diabetes-prediction-by-decision-tree-algorithm

e-commerce-customer-segmentation-by-kmeans-clustering

forecasting-sales-of-a-furniture-store

market-positioning-of-mobile-by-k-nearest-neighbors-classification

naive-bayes-classifier

predicting-term-deposit-subscription-by-a-client-by-svm-classifier

property-price-prediction-by-linear-regression

taxi-fare-prediction-by-random-forest-regressor

travel-review-segmentation-by-hierarchical-clustering-

vaccine-usage-prediction-by-logistic-regression

webscrapping-sentimentanalysis

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