VOICE AI BOT

#VoiceBOT #VoiceAI #VoiceOverShoppingAssistant #VoiceRecognition #MachineLearning #AI #DeepLearning

Created a Voice over Shopping BOT which can place order over Voice command .

In the 1st level, Rudra could :

1) Understand voice and take appropriate action e.g.     

 i) Shows the current time when asked the time    

ii) Opens up Google Map when asked for a place ( e.g. Where is Taj Mahal? )

 iii) Opens up the appropriate Shopping Platform ( e.g. #Amazon or #Myntra or #Jabong etc ) when asked to find the item - like find me a red shirt from Jabong .

iv) Can scan images among multiple images in a folder ( think from a UI perspective that you are uploading an image ) and find the appropriate item from the shopping platform.

In the 2nd Level, I had tried to make Rudra reaching towards the Goal like buying me an item . For example, if I say -Buy me a white Shirt from Myntra , it first opens up the appropriate search , then selects the item , then selects the appropriate size , adding it to shopping bag and placing the order  .

Due to security reasons, Myntra couldn't let me move to the final level  after multiple testing , which is understandable but Goal accomplished !

#github project  -  how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks. #tensorflow #keras #CNN #Neuralnet #INCEPTIONV3 #Machinelearning #DeepLearning

ImageClassificationDeepLearning

Here I will show how to loop through a folder containing multiple images and classifying them using Keras and Pre-trained Networks.

#TENSORFLOW #KERAS #NN #NEURALNET #INCEPTIONV3 #MACHINELEARNING #DEEPLEARNING

Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a jaguar, read a sign, or recognize a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images.

How it helps ? 

Let’s say you have a folder wherein Multiple images get uploaded – best example will be like OLX or Quickr which are free Buy & Sell websites. Now, you need to determine if any harmful things ( e.g. Gun etc) are being bought and sold .

SEVERAL PRE-TRAINED NETWORKS :

• VGG16, VGG19, ResNet50, Inception V3, and Xception

State-of-the-art deep learning image classifiers in Keras

Keras ships out-of-the-box with five Convolutional Neural Networks that have been pre-trained on the ImageNet dataset: VGG16 VGG19 ResNet50 Inception V3 Xception

Inception V3

The goal of the inception module is to act as a “multi-level feature extractor” by computing 1×1, 3×3, and 5×5 convolutions within the same module of the network — the output of these filters are then stacked along the channel dimension and before being fed into the next layer in the network. The original incarnation of this architecture was called GoogLeNet, but subsequent manifestations have simply been called Inception vN where N refers to the version number put out by Google.

Link :https://github.com/saptakbasu7/ImageClassificationDeepLearning 

Predictions and Suggestions from a machine learning based Algorithmic trading

#MachineLearning #AlgortihmicTrading #StockMarketAutomatedTrading  #LogisticRegression #Boosting

Predictions and Suggestions from a machine learning based Algorithmic trading

An algorithm is a specific set of clearly defined instructions aimed to carry out a task or process.

Algorithmic trading (automated trading, black-box trading, or simply algo-trading) is the process of using computers programmed to follow a defined set of instructions for placing a trade in order to generate profits at a speed and frequency that is impossible for a human trader. The defined sets of rules are based on timing, price, quantity or any mathematical model. Apart from profit opportunities for the trader, algo-trading makes markets more liquid and makes trading more systematic by ruling out emotional human impacts on trading activities.

We can create a Regression formula like below :

The dependent variable is the Return on capital invested and can be run across all stocks.

Error term ei can be boosted using Boosting Algos and thus increasing the prediction accuracy.

Now how to choose your Variables and what can be the ideal STOCK Equation :

YOY Quarterly sales growth  > 15 and

YOY Quarterly profit growth  > 20 and

Net Profit latest quarter  > 1 and

G Factor >= 7 and

Net Profit latest quarter > .33 AND

Other income latest quarter < Net Profit latest quarter * .5 AND

Net Profit preceding year quarter <= 0 AND

Expected quarterly net profit > 0 AND

Sales latest quarter > Sales preceding year quarter   AND

Return on invested capital > 25 and

Earnings yield > 15 and

Book value > 0 AND

Market Capitalization > 15

AND

Graham Number > Current price AND

PB X PE <=22.50 AND

PEG Ratio >0 AND

PEG Ratio <1 .5="" and="" o:p="">

Altman Z Score >=2.5 AND

Sales growth 5Years >25 AND

Profit growth 5Years >15 AND

Current ratio >2 AND

Market Capitalization >250 AND

Sales >100  AND

Piotroski score > 7

AND

Dividend yield > 2 AND

Average 5years dividend > 0 AND

Dividend last year > Average 5years dividend AND

Profit after tax > Net Profit last year * .8 AND

Dividend last year > .35 AND

( Profit growth 3Years > 10 OR

Profit growth 5Years > 10 OR

Profit growth 7Years > 10 ) 

OR

(Market Capitalization > 3000) AND

(Average return on equity 10Years Years > 20) AND

(Debt to equity < 1.5) AND

(Interest Coverage Ratio > 2) AND

( PEG Ratio <= 1) AND

(Profit growth 5Years > 20) 

AND

YOY Quarterly sales growth  > 40 and

YOY Quarterly profit growth  > 40 and

Average return on capital employed 3Years >30 and

Price to Earning <6 o:p="">

OR

Sales growth 10Years > 10 AND

Profit growth 10Years > 12 AND

OPM 10Year > 12 AND

Debt to equity < 0.5 AND

Current ratio > 1.5 AND

Altman Z Score > 3 AND

Average return on equity 10Years > 12 AND

Average return on capital employed 10Years >12 AND

Return on invested capital > 15 AND

Sales last year / Total Capital Employed > 2 AND

Average dividend payout 3years >15

AND

PEG Ratio <1 and="" o:p="">

Sales > 500 AND

Price to Earning < 40 AND

Profit growth > 20 AND

Debt to equity < 0.2 AND

Price to Cash Flow > 5

OR

EPS last year >20 AND

Debt to equity <.1 AND

Average return on capital employed 5Years >35 AND

Market Capitalization >500 AND

OPM 5Year >15

AND

Net Profit latest quarter > Net Profit preceding quarter AND

Net Profit preceding quarter > Net profit 2quarters back AND

Net profit 2quarters back > Net profit 3quarters back

AND

EPS latest quarter > 1.2 * EPS preceding year quarter AND

EPS latest quarter > 0 AND

YOY Quarterly sales growth > 25 AND

EPS last year > EPS preceding year AND

EPS > EPS last year AND

Profit growth 3Years > 25 AND

Return on equity > 17 AND

Down from 52w high < 15 AND

Market Capitalization > 100

AND

Price to Earning >0 and Price to Earning <10 5years="" and="" equity="" growth="" on="" return="">10 and Dividend yield >1 and Return on capital employed >10

AND

Profit growth 5Years > Sales growth 5Years AND

Sales growth 5Years > 3 AND

Return on equity > 15 AND

Working capital 5Years back < 0

AND

Price to Earning >0 and Return on equity 5years growth > 5 and Dividend yield >0  

Note : DEBT reacts inversely to the equation . Term period will be a spread over last 15 to 20 Years. 

Now , applying boosting algorithm ( like XGBoost) you can reduce the error coefficients.

Based on the above equation and a little variation choosing a flattened NN( Neural Network ) below stocks can be looked upon for Indian stock market.

1)  RELIANCE INDUSTRIES

2)  DCB BANK

3)  KAJARIA CERAMICS

4)  INFOSYS

5)  INDO COUNT INDUSTRIES