Taranjeet Singh

Car-Dekho-Price-Prediction-Regression-ML

February 3, 2021 – February 3, 2021

In this project prices are predicted for various 2nd hand car avaliable on car dekho website using regression.

Password-Strength-Prediction-NLP

February 3, 2021 – February 3, 2021

Its a password strength prediction model designed with the help of TF-IDF vectorizer which predicts whether the password strength is good or not

Flight-Fare-Prediction-regression-models

February 1, 2021 – February 1, 2021

After cleaning the data ,and encoding the categorical variables ,removing the outliers,and seeing feature importance and then depicting the flight fare of national flights with the help of regression models

Email-Spam-Classifier-Naive-Bayes-NLP-ML

December 24, 2020 – December 24, 2020

The SMS Spam Collection is a set of SMS tagged messages that have been collected for SMS Spam research. It contains one set of SMS messages in English of 5,574 messages, tagged according being ham (legitimate) or spam. The files contain one message per line. Each line is composed by two columns: v1 contains the label (ham or spam) and v2 contains the raw text. It will predict the correct classification of emails as SPAM or HEM ,developed with analysing on SMS Spam collection dataset

Traffic-Sign-Classification-LE-NET-Keras-Deep-Learning

December 24, 2020 – December 24, 2020

Traffic sign classification is an important task for self driving cars. In this project, a Deep Network known as LeNet will be used for traffic sign images classification. The dataset contains 43 different classes of images. Classes are as listed below: ( 0, b'Speed limit (20km/h)') ( 1, b'Speed limit (30km/h)') ( 2, b'Speed limit (50km/h)') ( 3, b'Speed limit (60km/h)') ( 4, b'Speed limit (70km/h)') ( 5, b'Speed limit (80km/h)') ( 6, b'End of speed limit (80km/h)') ( 7, b'Speed limit (100km/h)') ( 8, b'Speed limit (120km/h)') ( 9, b'No passing') (10, b'No passing for vehicles over 3.5 metric tons') (11, b'Right-of-way at the next intersection') (12, b'Priority road') (13, b'Yield') (14, b'Stop') (15, b'No vehicles') (16, b'Vehicles over 3.5 metric tons prohibited') (17, b'No entry') (18, b'General caution') (19, b'Dangerous curve to the left') (20, b'Dangerous curve to the right') (21, b'Double curve') (22, b'Bumpy road') (23, b'Slippery road') (24, b'Road narrows on the right') (25,

Chicago-Crime-Rate-Prediction-FBPROPHET-ML

December 24, 2020 – December 24, 2020

The Chicago Crime dataset contains a summary of the reported crimes occurred in the City of Chicago from 2001 to 2017. Dataset has been obtained from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. Dataset contains the following columns: ID: Unique identifier for the record. Case Number: The Chicago Police Department RD Number (Records Division Number), which is unique to the incident. Date: Date when the incident occurred. Block: address where the incident occurred IUCR: The Illinois Uniform Crime Reporting code. Primary Type: The primary description of the IUCR code. Description: The secondary description of the IUCR code, a subcategory of the primary description. Location Description: Description of the location where the incident occurred. Arrest: Indicates whether an arrest was made. Domestic: Indicates whether the incident was domestic-related as defined by the Illinois Domestic Violence Act. Beat: Indicates the beat where the incide

Convolutional-Neural-network-CNN-Deep-Learning

December 24, 2020 – December 24, 2020

A ML based project made with the help of one of the famous deep learning model Convolutional neural networks which determines whether the image shown is a picture belonging to dogs or a picture belonging to cat category

Restaurant-Reviews-Analysis-NLP-ML

December 24, 2020 – December 24, 2020

In this project sentiment Analysis of Restaurant reviews has been done. Firstly proper sentiment and tokenization of reviews has been done. all stopwords are removed then Naive Bayes Model has been trained with training data ( some reviews ) then we make predictions for the test data whether the reviews are negative or positive so it is a classification problem which is done using gaussian naive bayes.

Market-Basket-Optimization-Apriori-Analysis-ML

December 24, 2020 – December 24, 2020

The Apriori algorithm uses frequent itemsets to generate association rules, and it is designed to work on the databases that contain transactions. With the help of these association rule, it determines how strongly or how weakly two objects are connected. Frequent itemsets are those items whose support is greater than the threshold value or user-specified minimum support. It means if A & B are the frequent itemsets together, then individually A and B should also be the frequent itemset. Suppose there are the two transactions: A= {1,2,3,4,5}, and B= {2,3,7}, in these two transactions, 2 and 3 are the frequent itemsets. In this project the transactions of a store is noted for a week and apriori algorithm is used to make frequent item data so that a proper decision can be taken regarding those items which helps to make better offers for the customers which helps in increasing profit for the store.

Data-Structures-and-Algorithms

August 1, 2020 – August 1, 2020

These are the algorithms in C for the subject Design and Analysis of algorithms.