Sometimes Deep Sometimes Learning

Experimenting with Machine Learning and Deep Learning in Python.

I mostly use Jupyter Notebook, with Anaconda distribution of Python when it comes to DS/ML/DL.

Kaggle Profile : jaivarsan.

Analytics Vidhya Profile : greed2411.

Libraries used :

scikit-learn : for purely Machine Learning Algorithms and preprocessing.
pandas : for data analysis purposes.
seaborn : for beautiful visualizations.
plotly : for time series graphs.
gensim : for NLP.
keras : for deep learning purposes.
tensorflow : for keras backend.
pytorch : cause the hype is real.

Papers Implemented :

K. Altun, B. Barshan, and O. Tunçel, Comparative study on classifying human activities with miniature inertial and magnetic sensors, Pattern Recognition, 43(10):3605-3620, October 2010.

for UCI ML repositories : Daily and Sports Activities Data Set report and files available over here

Time for visualizations:

On Miscellaneous

Indian map plot of all the 498 cities mentioned in the NDL dataset from Kaggle:

Heat map from california house pricing dataset:

Object Detection using TensorFlow for both pictures and Video, for our Microprocessor Project using RPi:

Me and my friends, during a trek:

Friend of mine got caught on the RPi cam feed:

The famous iris dataset

Andrews curves

Parallel Plotting
Pairplot

Data is Beautiful, Not Saturn's rings

On Healthcare

Breast Cancer

Melanoma Prediction

Cycling and Calories dataset

On Entertainment

The time when i solved math memes from facebook

In [28]: best_model_pred

Out[28]:

['7+2+5 = 107687\n',
 '7+2+5 = 191991\n',
 '7+2+5 = 202541\n',
 '7+2+5 = 18908\n',
 '7+2+5 = 183652\n']

On Business

Scatterplot on Relationship between Sales because of TV, Radio and Newspaper.

Plotly graphs of the recent trends in Bitcoin world, intereactive time series graphs.

Just to give you a tease:

This is the first dataset I handled, xD.

While working on NDL, A Kaggle dataset

I made the following observations:

Analysis made on the data set as of June 2017

Number of cities : 493
Number of states : 29
Most number of cities are in the state: UTTAR PRADESH
Number of cities in UTTAR PRADESH : 63

Least number of cities belong to these states and their counts

HIMACHAL PRADESH             1
CHANDIGARH                   1
TRIPURA                      1
MIZORAM                      1
NAGALAND                     1
MANIPUR                      1
MEGHALAYA                    1
ANDAMAN & NICOBAR ISLANDS    1

There are two Aurangabad(s) in the nation,
- one belonging to BIHAR
- second one belonging to MAHARASHTRA

Top 5 States with the maximum number of Cities

  UTTAR PRADESH     63
  WEST BENGAL       61
  MAHARASHTRA       43
  ANDHRA PRADESH    42
  TAMIL NADU        32

States vs City counts
States vs District counts
Each city and it's district number plot

This graph made me analyse and conclude that
- The most common district numbers are 11, 9 and 12 not the conventional 1, 2 and 3.
- District Number, and their occurences and percentage they contribute to the total district count. Example : District Number 11, there are 37 districts in our India numbered 11, which contributes to 7.51% of total number of districts in India.
```
                             District Counts  Percentage Index
 District Number                                   
 11                            37              7.51
 9                             26              5.27
 12                            24              4.87
 1                             22              4.46
 3                             22              4.46
 21                            21              4.26
```
- 95.94% of districts have their district number value which is less than 50
District numbers and their frequency
- The above graphs tells us that there are no district numbers from 72 to 98 and few numbers here and there in the 40s & 50s
  
  Actual missing district numbers :
```
 40, 42, 43, 45, 51, 53, 55, 56, 58, 67, 69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98
```
Missing State Codes
```
 11, 12, 25, 26, 30, 31
```

greed2411 / ML