->Highly analytical individual with strong apt for learning and collaborative skills.
->Looking to obtain a Data Analyst position, to utilize my skills and what i have taught till date.
->Offering expertise in Machine Learning and analyzing large complex and multi dimensional data set using a variety of tools.
Tableau
PROJECT 1- PREDICT A NON-BANKING FINANCIAL COMPANY BORROWER TYEP FOR DIRECTLY INCREASE IN BUSINESS GROWTH.
- PeerLoanKart is an NBFC (Non-Banking Financial Company) which facilitates peer to peer loan.
- It connects people who need money (borrowers) with people who have money (investors). As an investor, you would want
to invest in people who showed a profile of having a high probability of paying you back.
DATA VOLUME:- Approx 9578 records
USED TOOLS:-
1- Jupyter Notebook (IDE for Python) .
2- Pandas ( For Data Analysis)
3- Numpy (For Mathematical Operations)
4- SK Learn (For Machine Learning) / SVC model
5- Matplotlib (For Data Visualization)
6- Seaborn (For Data Visualization)
RESULT:- I have created a classifier model with 83% accuracy that will help predict whether a borrower will pay the loan or not
and Increase in profits up to 20% as NPA will be reduced due to loan disbursal for only good borrowers.
PROJECT 2 - PREDICT THE CANDIDATE VOICE AS MALE OR FEMALE FOR THE " THE STAR RJ " REALITY SHOW SO THE FIRST LEVEL
OF FILTRATION IS QUICKER BECAUSE THE WHOLE SUCCESS OF THE SHOW AND HENCE THE PROFIT DEPENDS UPON QUICK AND SMOOTH EXECUTION.
- Motion Studios is the largest Radio production house in Europe. Their total revenue $ 1B+. Company has launched a new
reality show – "The Star RJ"
- The show is about finding a new Radio Jockey who will be the star presenter on upcoming shows.
- In first round participants have to upload their voice clip online and the clip will be evaluated by experts for selection into the next round.
- So in the first round for as a Data Analyst i have to evaluate male and female voice. .
DATA VOLUME:- Approx 3000 records
USED TOOLS:-
1- Jupyter Notebook (IDE for Python).
2- Pandas ( For Data Analysis)
3- Numpy (For Mathematical Operations)
4- SK Learn (For Machine Learning) / SVC model
5- Matplotlib (For Data Visualization)
6- Seaborn (For Data Visualization)
RESULT:- I have created a classifier model with 92% accuracy that will help predict whether the Candidate voice is male of female and faster the
first Round selection.
PROJECT 3 :- GROUPING TEH DRIVERS (OF A ELECTRIC VEHICLE BATTERY COMPANY WHO PROVIDES BATTERIES ON RENTAL MODEL TO
E-VEHICLE DRIVERS ) WHO WILL BE INCENTIVIZED BASED TO THE CLUSTERS.
- Rivine Automotive company is the largest provider of electric vehicle(e-vehicle) batteries on rental model to e_vehicle drivers.
- Drivers rent battery typically for a day and then replace it with a charged battery from the company.
- Rivine Automotive company has a variable pricing model based on driver's driving history. As the life of a battery depends on factors such as
over speeding, distance driven per day etc.
- So as a Data Analyst i have to grouping the drivers in accurate clusters.
DATA VOLUME:- Approx 4000 records.
USED TOOLS:-
1- Jupyter Notebook (IDE for Python).
2- Pandas ( For Data Analysis)
3- Numpy (For Mathematical Operations)
4- SK Learn (For Machine Learning) / Kmeans Clustering model
5- SNS.SET (For Plot Styling)
6- Matplotlib (For Data Visualization)
7- Seaborn (For Data Visualization)
RESULT:- I have successfully grouping the drivers into clusters who will incevitized.
WORKSHOP :- Attended a Workshop on introduction to globally used Analytics instruments during National Pharmacy Week.