A Better World : Analysis on Kiva Loan Data

An exploratory analysis on Kiva loan data found here on Kaggle.com.

Project Breakdown

Overall picture of the statistics of kiva loans all over the world(eg: loan_amount, gender split, activity, sectors, etc)
Have a base world map which can be zoomed on each continent and be able to click on each country which would pop up the existing statistics on Kiva data Countrywise (eg: loan_amount, most_active_sector )
Kiva growth over the years (Number of loans/Total loan amount over the years, stacked by sector)
Gender disparity by regions (Map) & sectors, overall growth through time(Pie/bar/line charts)
Overall number of loans, sum of loans, Loans by sector (different layers in map)
Timeline (animated)… Loans by time , color coded by sector, size by loan amount.
Have a base world map which can be zoomed on each continent and be able to click on each country which would pop up the existing statistics on Kiva data Countrywise (eg: loan_amount, most_active_sector )

Show correlation between gender and kiva loans and come up with other correlations (we will work on this using tableau and come up with insights)
Where could the money lended by lenders be efficiently utilized ? (Measure by growth/poverty)
Deeper look at Philippines, Kenya & Peru (Top 3 by number of loans) By sector, activity, repayment. Scrape local news around the time of much increase in loans (Is it Kiva popularity or is it the need?) .

Deeper look at Last three, budding regions. Scope for more funding? (How much do they need really?)

Mid three? How can we improve ? Do they need? (Verify with poverty index )
Lending to teams vs Lending to individuals, in what all sectors does it make a difference?

Predict where could the money lended by lenders be efficiently utilized (to be decided)
Will a loan money returned, be refunded into other loans?? (How to maximize refunding? Where they refund ? same sector?)
How many borrowers might come forward to fulfill a new loan?
Group the loans or borrowers into clusters by Unsupervised learning techniques and identify top n features (K-Means)

Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
img		img
static		static
templates		templates
1706.02795.pdf		1706.02795.pdf
Clustered_samples_dataset.csv		Clustered_samples_dataset.csv
Final-v3.pdf		Final-v3.pdf
Kiva_4_Clusters.pkl		Kiva_4_Clusters.pkl
OptimizingNumberOfClusters_KMeans-Presentation.ipynb		OptimizingNumberOfClusters_KMeans-Presentation.ipynb
OptimizingNumberOfClusters_KMeans.ipynb		OptimizingNumberOfClusters_KMeans.ipynb
Procfile		Procfile
README.md		README.md
app.py		app.py
flag_scrape.py		flag_scrape.py
json_builder.py		json_builder.py
mpi.csv		mpi.csv
mytestutilities.py		mytestutilities.py
requirements.txt		requirements.txt
runtime.txt		runtime.txt