Add Social Network Analysis Project to Unsupervised Learning and Blood Donations Prediction to Prediction Models Module

Prediction Models/Blood Donations Prediction/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Souvik Banerjee
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Prediction Models/Blood Donations Prediction/README.md

@@ -0,0 +1,60 @@
+# Predict-Blood-Donations [![](https://img.shields.io/github/license/sourcerer-io/hall-of-fame.svg?colorB=ff0000)](https://github.com/souvikb07/Predict-Blood-Donations/blob/master/LICENSE)  [![](https://img.shields.io/badge/Souvik-Banerjee-blue.svg)](https://souvikb07.github.io)
+
+<img src = "https://github.com/souvikb07/souvikb07.github.io/blob/master/images/blood_donationcover.jpeg">
+
+## Problem
+Predicting if a Blood Donor will donate within a given time window?
+
+### Application
+By solving this problem the blood donation camps can get 40% more blood donors.
+
+### Sourcerer
+[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/0)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/0)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/1)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/1)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/2)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/2)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/3)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/3)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/4)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/4)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/5)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/5)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/6)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/6)[![](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/images/7)](https://sourcerer.io/fame/souvikb07/souvikb07/Predict-Blood-Donations/links/7)
+
+### Code Requirements
+The example code is in Python ([version 3.6.6](https://www.python.org/downloads/release/python-366/) or higher will work). 
+
+### Dependencies
+
+1) import pandas
+2) import numpy
+3) import seaborn
+4) import matplotlib
+5) import Counter
+6) import sklearn
+
+## Description
+
+Use Python to explore data related to blood donors and we want to predict whether or not a donor will give blood the next time when the blood donation will be organised.
+
+* This process will be done using a Jupyter Notebook.
+* The code should run w/o errors.
+* Appropriate use of
+    * data structures/types
+    * loops/conditional statements
+    * Packages
+    * functions
+    * coding practices (i.e. Docstrings, comments, variable names & general
+      readability)
+* Analysis
+    * Pose questions about the data
+    * Inspect the structure of the original data (very important)
+    * Clean the data
+    * Answer questions about the data using descriptive statistics
+    * Visualize the data (using plt and seaborn)
+    * Perform additional exploratory analysis
+    * Consider where data analysis can be applied to other fields.
+* Feature Engineering
+    * Using domain knowledge of the data to create features that make machine learning algorithms work. 
+* Machine Learning
+    * Use of Random Forest, Extra Trees, Gradient Boosting, SVC classifiers.
+
+## File Descriptions
+
+* ./data/ contains the various datasets.
+* ./Blood Donation Prediction.ipynb is a Jupyter notebook containing the work I have done.
+
+## References & Citations
+
+1. Data provided by:
+    * [drivendata.org](https://www.drivendata.org/competitions/2/warm-up-predict-blood-donations/data/)

Prediction Models/Blood Donations Prediction/data/test.csv

@@ -0,0 +1,201 @@
+,Months since Last Donation,Number of Donations,Total Volume Donated (c.c.),Months since First Donation
+659,2,12,3000,52
+276,21,7,1750,38
+263,4,1,250,4
+303,11,11,2750,38
+83,4,12,3000,34
+500,3,21,5250,42
+530,4,2,500,4
+244,14,1,250,14
+249,23,2,500,87
+728,14,4,1000,64
+129,13,3,750,16
+534,11,7,1750,62
+317,5,11,2750,75
+401,4,1,250,4
+696,4,4,1000,26
+192,11,1,250,11
+176,11,6,1500,26
+571,7,14,3500,48
+139,23,14,3500,93
+423,3,4,1000,29
+563,2,7,1750,29
+56,4,6,1500,35
+528,5,7,1750,26
+101,4,1,250,4
+467,2,3,750,38
+382,5,14,3500,86
+466,2,2,500,11
+294,14,1,250,14
+512,4,3,750,16
+659,2,12,3000,52
+389,4,14,3500,86
+487,23,7,1750,88
+701,2,1,250,2
+419,4,7,1750,58
+536,4,2,500,41
+240,11,7,1750,29
+508,2,2,500,41
+515,14,1,250,14
+283,4,6,1500,28
+650,4,1,250,4
+65,2,2,500,4
+228,16,6,1500,35
+741,16,6,1500,81
+297,2,5,1250,26
+464,14,3,750,31
+63,2,1,250,2
+231,4,4,1000,14
+28,14,3,750,35
+248,14,1,250,14
+357,14,5,1250,28
+300,2,14,3500,57
+726,5,24,6000,79
+680,14,4,1000,23
+520,4,6,1500,39
+254,23,2,500,38
+582,11,8,2000,52
+143,2,7,1750,77
+98,4,5,1250,11
+1,2,4,1000,35
+221,2,15,3750,64
+352,4,1,250,4
+64,11,2,500,38
+138,12,15,3750,71
+745,2,13,3250,76
+64,11,2,500,38
+688,16,2,500,27
+623,9,4,1000,65
+289,21,16,4000,64
+174,7,10,2500,47
+690,4,1,250,4
+105,11,5,1250,35
+427,16,4,1000,23
+48,38,1,250,38
+14,4,1,250,4
+657,23,4,1000,52
+301,11,7,1750,64
+455,14,3,750,28
+579,4,11,2750,78
+722,16,4,1000,33
+98,4,5,1250,11
+491,38,1,250,38
+303,11,11,2750,38
+466,2,2,500,11
+65,2,2,500,4
+300,2,14,3500,57
+9,21,1,250,21
+622,11,1,250,11
+323,16,2,500,26
+289,21,16,4000,64
+568,2,4,1000,26
+290,4,1,250,4
+156,4,2,500,52
+464,14,3,750,31
+426,16,2,500,16
+306,16,11,2750,40
+4,11,11,2750,42
+12,4,5,1250,23
+187,11,12,3000,58
+406,23,8,2000,64
+96,4,2,500,4
+509,2,1,250,2
+733,11,6,1500,58
+548,21,3,750,35
+478,7,5,1250,35
+501,11,2,500,16
+127,2,4,1000,11
+199,15,16,4000,82
+299,2,10,2500,49
+162,4,1,250,4
+235,16,3,750,21
+23,9,2,500,16
+473,4,1,250,4
+487,23,7,1750,88
+683,4,8,2000,28
+303,11,11,2750,38
+309,23,3,750,48
+569,23,1,250,23
+34,4,7,1750,28
+686,4,16,4000,38
+84,2,2,500,23
+733,11,6,1500,58
+537,4,9,2250,26
+181,11,4,1000,34
+453,0,26,6500,76
+67,2,10,2500,52
+161,2,16,4000,81
+307,4,6,1500,46
+703,11,2,500,11
+181,11,4,1000,34
+246,14,2,500,14
+316,3,5,1250,26
+278,11,1,250,11
+346,11,3,750,15
+545,4,1,250,4
+419,4,7,1750,58
+694,4,2,500,4
+622,11,1,250,11
+663,11,3,750,76
+262,2,13,3250,32
+461,2,12,3000,98
+373,2,34,8500,77
+233,4,1,250,4
+466,2,2,500,11
+207,2,7,1750,77
+263,4,1,250,4
+16,23,1,250,23
+513,23,3,750,35
+449,4,5,1250,33
+429,23,3,750,62
+701,2,1,250,2
+632,21,2,500,41
+529,2,9,2250,22
+245,11,9,2250,33
+344,16,6,1500,40
+353,16,3,750,19
+241,8,15,3750,77
+633,16,1,250,16
+624,2,1,250,2
+726,5,24,6000,79
+189,11,1,250,11
+138,12,15,3750,71
+402,14,1,250,14
+511,4,6,1500,23
+590,9,9,2250,16
+334,2,3,750,52
+447,16,3,750,50
+119,2,2,500,11
+389,4,14,3500,86
+644,4,1,250,4
+423,3,4,1000,29
+131,23,1,250,23
+405,40,1,250,40
+82,4,1,250,4
+643,2,41,10250,98
+156,4,2,500,52
+617,21,3,750,38
+574,2,3,750,9
+272,2,12,3000,95
+613,11,2,500,52
+545,4,1,250,4
+685,14,1,250,14
+570,11,5,1250,33
+537,4,9,2250,26
+691,16,1,250,16
+85,4,16,4000,70
+483,14,2,500,14
+455,14,3,750,28
+93,11,1,250,11
+744,2,1,250,2
+33,14,2,500,14
+321,4,23,5750,58
+523,6,3,750,26
+426,16,2,500,16
+196,14,2,500,29
+301,11,7,1750,64
+103,16,1,250,16
+224,21,2,500,23
+454,23,8,2000,46
+585,23,2,500,28
+154,4,11,2750,64