Waaris
Founder
Vaibhav Chawla
Enabler at Wherehouse.io
Holding a graduate degree from DTU and experience in research and early-stage startups, I bring with me skills in research, leadership
Enabler at Wherehouse.io
Holding a graduate degree from DTU and experience in research and early-stage startups, I bring with me skills in research, leadership
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Experience
Founder
Co-founder
Wherehouse.io is a technology driven network of micro-warehouses across India’s 12+ cities. The platform allows brands to identify customer clusters and intelligently place... more inventory closer to them. With deep ML algorithms, the cloud engine weaves the network of hyperlocal and 3PL partners to offer same day, next day delivery cost-efficiently. Building on the core values of control, visibility and delight, the democratic platform has scaled a network of 2500+ warehouses across 12+ cities, utilising the vacant commercial capacity of India. The network empowers the local entrepreneurs to supplement their income while enabling brands to reach closer to the customer and deliver faster. Driven by proprietary technology, the warehouses allow superior quality control and operational procedures that makes the experience more personal and delightful for the brands and their end customers.
National Council of Cement and Building Materials
Project Engineer (R&D)
Development of a model to predict strength of cement based upon analysis of over 15 physical and chemical parameters of cement samples in python.
Analysis of variation... more of quality of cement with addition of supplementary materials like Fly ash, Slag and preparing a cost-strength model to predict optimum content of supplementary materials.
Successfully completed multi-million dollar government projects, providing technical assistance and operational management of their projects.
Analysis of variation... more of quality of cement with addition of supplementary materials like Fly ash, Slag and preparing a cost-strength model to predict optimum content of supplementary materials.
Successfully completed multi-million dollar government projects, providing technical assistance and operational management of their projects.
TOROSHU
Strategist
Served as co-founding member and received INR 1.5 million as Bootstrap funding from Facebook.
Analyzed data on India's wedding industry using MS-Excel and laid out data-driven... more strategies for market penetration of wedding industry aggregator product.
Successfully created a platform for service providers and customers based on data driven decisions.
Analyzed data on India's wedding industry using MS-Excel and laid out data-driven... more strategies for market penetration of wedding industry aggregator product.
Successfully created a platform for service providers and customers based on data driven decisions.
Founder
Employee
Projects
To create a model to preidct strenth of cement
Data Analyst , Python, Random Forests, Python, Pandas, Seaborn · Analysis of more than 15 physical and chemical parameters of cement samples from more… · More than 2000 sources from India.
Finding relationship between various chemical and physical parameters of cement and testifying the theoretical assumptions.
Creating a model to predict strength of cement based on the above analysis.
Analysis of more than 15 physical and chemical parameters of cement samples from more than 2000 sources from India.
Finding relationship between various chemical and physical parameters of cement and testifying the theoretical assumptions.
Creating a model to predict strength of cement based on the above analysis.
Finding relationship between various chemical and physical parameters of cement and testifying the theoretical assumptions.
Creating a model to predict strength of cement based on the above analysis.
Analysis of more than 15 physical and chemical parameters of cement samples from more than 2000 sources from India.
Finding relationship between various chemical and physical parameters of cement and testifying the theoretical assumptions.
Creating a model to predict strength of cement based on the above analysis.
To create a model to predict cost of cement
Data Analyst, Python, Machine Learning, Seaborn · Preparing a database of cement quality/strength parameters and composition of supplementary materials… · More (Fly ash, Slag) materials present in it.
Analysis of variation of quality and cost of cement with different composition of supplementary materials.
Preparing a cost strength model to predict optimum composition of cement mix.
Preparing a database of cement quality/strength parameters and composition of supplementary materials (Fly ash, Slag) materials present in it.
Analysis of variation of quality and cost of cement with different composition of supplementary materials.
Preparing a cost strength model to predict optimum composition of cement mix.
Analysis of variation of quality and cost of cement with different composition of supplementary materials.
Preparing a cost strength model to predict optimum composition of cement mix.
Preparing a database of cement quality/strength parameters and composition of supplementary materials (Fly ash, Slag) materials present in it.
Analysis of variation of quality and cost of cement with different composition of supplementary materials.
Preparing a cost strength model to predict optimum composition of cement mix.
To prepare a classifier to classify iris flowers
Data Analyst, Python, Machine Learning, Decision Trees · Cleaned and Analysed Iris dataset using python libraries Pandas and Numpy.
Used seaborn… · More and matplotlib libraries to create more than 10 plots during analysis.
Tested and Designed “Random Forest” and “Decision Tree Classifier” to identify Iris flower.
Cleaned and Analysed Iris dataset using python libraries Pandas and Numpy.
Used seaborn and matplotlib libraries to create more than 10 plots during analysis.
Tested and Designed “Random Forest” and “Decision Tree Classifier” to identify Iris flower.
Used seaborn… · More and matplotlib libraries to create more than 10 plots during analysis.
Tested and Designed “Random Forest” and “Decision Tree Classifier” to identify Iris flower.
Cleaned and Analysed Iris dataset using python libraries Pandas and Numpy.
Used seaborn and matplotlib libraries to create more than 10 plots during analysis.
Tested and Designed “Random Forest” and “Decision Tree Classifier” to identify Iris flower.
Use Machine Learning skills to create a POI classifier
Data Analyst, Decision Trees, Machine Learning, Support Vector Machines · The goal is to play detective and explore Python's various machine… · More learning tools and frameworks to identify persons of interest (POIs) in the Enron corporate fraud case.
1. We were given a dataset with 146 data points (i.e. "people"), each of which has 21 features.
2. Of the 146 people in the data set, there were 18 POIs. Using machine learning to identify the POIs is useful because of the complexity of the data set.It allows us to try to find patterns to detect POIs.
3. A model was created which help us identify POIs from new data means, if a new person and their data are sent through the model, the model can then identify whether that new person may be a POI or not.
Tools/Methodologies : Python, Machine Learning Algorithm(K-Nearest Neighbor Algorithm)
Used Machine Learning skills to create a POI classifier
1. We were given a dataset with 146 data points (i.e. "people"), each of which has 21 features.
2. Of the 146 people in the data set, there were 18 POIs. Using machine learning to identify the POIs is useful because of the complexity of the data set.It allows us to try to find patterns to detect POIs.
3. A model was created which help us identify POIs from new data means, if a new person and their data are sent through the model, the model can then identify whether that new person may be a POI or not.
Tools/Methodologies : Python, Machine Learning Algorithm(K-Nearest Neighbor Algorithm)
Used Machine Learning skills to create a POI classifier
Data Analysis with python
Data Analyst, Python NumPy, Python, Pandas, Seaborn · 1. Performed thorough investigation of famous Titanic data from kaggle using python libraries… · More Pandas and Numpy by posing questions.
2. Used seaborn and matplotlib libraries to create more than 15 plots during analysis.
Tools/Methodologies : Python
2. Used seaborn and matplotlib libraries to create more than 15 plots during analysis.
Tools/Methodologies : Python
Open-source anti theft app with 3 tier securities for phone
Tomato Safety is an android app which can help you in protecting your smartphone from being stolen.
It provides three tier of security to your… · More phone:
1. sim change notifications
2. remotely tracking of your phone
3. starting siren by the SMS.
Github link: https://github.com/sainihimanshu/TomatoApp
It provides three tier of security to your… · More phone:
1. sim change notifications
2. remotely tracking of your phone
3. starting siren by the SMS.
Github link: https://github.com/sainihimanshu/TomatoApp
Data Wrangling
Data Analyst, Python, SQLite, Data Wrangling · 1. Employed Data Wrangling skills to audit and clean the OpenStreetMap data(around 1 GB) for… · More Rohini,Delhi.
2. Parsed and gathered data from popular file formats such as JSON, XML, CSV, HTML
3. Processed data from many files and very large files that can be cleaned with spreadsheet programs
4. Used data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity.
5. Used SQL in python to run queries on the cleaned data set to explore its features.
Tools/Methodologies: Python, MongoDB, SQL
2. Parsed and gathered data from popular file formats such as JSON, XML, CSV, HTML
3. Processed data from many files and very large files that can be cleaned with spreadsheet programs
4. Used data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity.
5. Used SQL in python to run queries on the cleaned data set to explore its features.
Tools/Methodologies: Python, MongoDB, SQL
Education
About
Skills
Locations
Q&A
What's the most useful business-related book you've ever read?
Zero to one by Peter Thiel
What's your favorite non-business book?
Kite runner