Pinned Repositories
aws-machine-learning-university-accelerated-cv
Machine Learning University: Accelerated Computer Vision Class
complete-javascript-course
Starter files, final projects, and FAQ for my Complete JavaScript course
data-visualization-on-Chicago-COPA-Cases-Dataset
Depression-Assistant-Chatbot
In this project, I extend the implementation of a Tweet/Sentence Sentiment Classification to a "Depression Assistant Chatbot". This software asks the users how they are feeling and if what they write expresses sadness of anger then they are greeted with jokes until they feel better.
hello_test
Household-Electric-Power-Consumption
Data Set Information: This archive contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Notes: 1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. 2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007. Attribute Information: 1.date: Date in format dd/mm/yyyy 2.time: time in format hh:mm:ss 3.global_active_power: household global minute-averaged active power (in kilowatt) 4.global_reactive_power: household global minute-averaged reactive power (in kilowatt) 5.voltage: minute-averaged voltage (in volt) 6.global_intensity: household global minute-averaged current intensity (in ampere) 7.sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). 8.sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. 9.sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.
Insurance-Forecast-by-using-Linear-Regression
Insurance-Forecast-by-using-Linear-Regression on Medical Cost Personal Datasets
Inuvest-Assessment-test-
ADX indicator
libbuhlmann
Library for diver decompression model calculations.
Statistics-on-Breast-Cancer-Wisconsin-Diagnostic-Data-Set
Nishantsorathiya04's Repositories
Nishantsorathiya04/Statistics-on-Breast-Cancer-Wisconsin-Diagnostic-Data-Set
Nishantsorathiya04/aws-machine-learning-university-accelerated-cv
Machine Learning University: Accelerated Computer Vision Class
Nishantsorathiya04/complete-javascript-course
Starter files, final projects, and FAQ for my Complete JavaScript course
Nishantsorathiya04/data-visualization-on-Chicago-COPA-Cases-Dataset
Nishantsorathiya04/Depression-Assistant-Chatbot
In this project, I extend the implementation of a Tweet/Sentence Sentiment Classification to a "Depression Assistant Chatbot". This software asks the users how they are feeling and if what they write expresses sadness of anger then they are greeted with jokes until they feel better.
Nishantsorathiya04/hello_test
Nishantsorathiya04/Household-Electric-Power-Consumption
Data Set Information: This archive contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Notes: 1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. 2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007. Attribute Information: 1.date: Date in format dd/mm/yyyy 2.time: time in format hh:mm:ss 3.global_active_power: household global minute-averaged active power (in kilowatt) 4.global_reactive_power: household global minute-averaged reactive power (in kilowatt) 5.voltage: minute-averaged voltage (in volt) 6.global_intensity: household global minute-averaged current intensity (in ampere) 7.sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). 8.sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. 9.sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.
Nishantsorathiya04/Insurance-Forecast-by-using-Linear-Regression
Insurance-Forecast-by-using-Linear-Regression on Medical Cost Personal Datasets
Nishantsorathiya04/Inuvest-Assessment-test-
ADX indicator
Nishantsorathiya04/libbuhlmann
Library for diver decompression model calculations.
Nishantsorathiya04/Machine-Hack
Predicting The Costs Of Used Cars - Hackathon By Imarticus Learning
Nishantsorathiya04/profile-rest-api
source code for profiles rest api
Nishantsorathiya04/Underwater-Color-Correction
Using GANs to correct color distortion in underwater images.
Nishantsorathiya04/Weight-Prediction-on-Fish-market-Dataset