This Model Predicts whether the image is of a cat or a dog by using Convolution Neural Network.

About the Data

The Data Contains 24960 images of both Cats and Dogs.

About the Model

This Model differentiates whether an image is of a cat or dog using Convolutional Neural Networks. This model is ready to be used in IOT devices with the help of deepCC framework which converts this model in an executable file which can then be used in embedded devices such as Arduino, MCU’s,etc. Here is one of the many ways to approach this problem.

Importing Libraries

Importing necessary dependencies which will help us to do various operation on the data and reach our goal i.e. to predict whether the image is of a cat or a dog.

Loading the Data

Data Pre-processing

we use ImageDataGenerator to scale the pixel size, change the color of the image from BGR to Gray and also to split the Data in Training and Validation set.

Building the Model

Since the Data is in image form I’m using Convolutional Neural Network in order to find different patterns in the image which will help in differentiating the labels(cats&dogs).

Compiling and Fitting the Model

Plotting Accuracy

Plotting Loss

Model Predictions

Here are some predictions made on the Test Images.

The Images in Test Images look like this because the Images are resized to size 50 x 50 but still our model can find pattern in the images which can differentiate between the labels.

Saving the Model and deepCC

Conclusion

Through this Model we can determine whether the image is of a cat or a dog using Deep Learning.

deepCC framework converts this model to an executable file through which this file can run in Embeded Devices like Arduino, Raspberry.pi, etc

Link to Notebook- https://cainvas.ai-tech.systems/notebooks/details/?path=tanmay/catsvsdogs.ipynb

Cats vs Dogs Classification was originally published in AITS Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.

The dataset consists of 70 000 records of patients data in 12 features, such as age, gender, systolic blood pressure, diastolic blood pressure, and etc. The target class “cardio” equals to 1, when patient has cardiovascular disease, and it’s 0, if patient is healthy.

Data description

There are 3 types of input features:

• Objective: factual information;
• Examination: results of medical examination;
• Subjective: information given by the patient.

Features:

1. Age | Objective Feature | age | int (days)
2. Height | Objective Feature | height | int (cm) |
3. Weight | Objective Feature | weight | float (kg) |
4. Gender | Objective Feature | gender | categorical code |
5. Systolic blood pressure | Examination Feature | ap_hi | int |
6. Diastolic blood pressure | Examination Feature | ap_lo | int |
7. Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |
8. Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |
9. Smoking | Subjective Feature | smoke | binary |
10. Alcohol intake | Subjective Feature | alco | binary |
11. Physical activity | Subjective Feature | active | binary |
12. Presence or absence of cardiovascular disease | Target Variable | cardio | binary |

Importing Libraries

Loading the Data

Scaling the Data(to avoid outliers)

Splitting the Data

Building the Model

Fitting the Model

Plotting Accuracy and Loss

Saving and deepCC

Conclusion

This Model predicts whether the patient has any Cardio- Vascular Disease or not using Deep Neural Networks.

Link to the Notebook

https://cainvas.ai-tech.systems/notebooks/details/?path=tanmay/cardio_vascular.ipynb

Cardio- Vascular Disease Prediction was originally published in AITS Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Context

Access to safe drinking-water is essential to health, a basic human right and a component of effective policy for health protection. This is important as a health and development issue at a national, regional and local level. In some regions, it has been shown that investments in water supply and sanitation can yield a net economic benefit, since the reductions in adverse health effects and health care costs outweigh the costs of undertaking the interventions.

Content

The water_potability.csv file contains water quality metrics for 3276 different water bodies.

1. pH value:

PH is an important parameter in evaluating the acid–base balance of water. It is also the indicator of acidic or alkaline condition of water status. WHO has recommended maximum permissible limit of pH from 6.5 to 8.5. The current investigation ranges were 6.52–6.83 which are in the range of WHO standards.

2. Hardness:

Hardness is mainly caused by calcium and magnesium salts. These salts are dissolved from geologic deposits through which water travels. The length of time water is in contact with hardness producing material helps determine how much hardness there is in raw water. Hardness was originally defined as the capacity of water to precipitate soap caused by Calcium and Magnesium.

3. Solids (Total dissolved solids — TDS):

Water has the ability to dissolve a wide range of inorganic and some organic minerals or salts such as potassium, calcium, sodium, bicarbonates, chlorides, magnesium, sulfates etc. These minerals produced un-wanted taste and diluted color in appearance of water. This is the important parameter for the use of water. The water with high TDS value indicates that water is highly mineralized. Desirable limit for TDS is 500 mg/l and maximum limit is 1000 mg/l which prescribed for drinking purpose.

4. Chloramines:

Chlorine and chloramine are the major disinfectants used in public water systems. Chloramines are most commonly formed when ammonia is added to chlorine to treat drinking water. Chlorine levels up to 4 milligrams per liter (mg/L or 4 parts per million (ppm)) are considered safe in drinking water.

5. Sulfate:

Sulfates are naturally occurring substances that are found in minerals, soil, and rocks. They are present in ambient air, groundwater, plants, and food. The principal commercial use of sulfate is in the chemical industry. Sulfate concentration in seawater is about 2,700 milligrams per liter (mg/L). It ranges from 3 to 30 mg/L in most freshwater supplies, although much higher concentrations (1000 mg/L) are found in some geographic locations.

6. Conductivity:

Pure water is not a good conductor of electric current rather’s a good insulator. Increase in ions concentration enhances the electrical conductivity of water. Generally, the amount of dissolved solids in water determines the electrical conductivity. Electrical conductivity (EC) actually measures the ionic process of a solution that enables it to transmit current. According to WHO standards, EC value should not exceeded 400 μS/cm.

7. Organic_carbon:

Total Organic Carbon (TOC) in source waters comes from decaying natural organic matter (NOM) as well as synthetic sources. TOC is a measure of the total amount of carbon in organic compounds in pure water. According to US EPA < 2 mg/L as TOC in treated / drinking water, and < 4 mg/Lit in source water which is use for treatment.

8. Trihalomethanes:

THMs are chemicals which may be found in water treated with chlorine. The concentration of THMs in drinking water varies according to the level of organic material in the water, the amount of chlorine required to treat the water, and the temperature of the water that is being treated. THM levels up to 80 ppm is considered safe in drinking water.

9. Turbidity:

The turbidity of water depends on the quantity of solid matter present in the suspended state. It is a measure of light emitting properties of water and the test is used to indicate the quality of waste discharge with respect to colloidal matter. The mean turbidity value obtained for Wondo Genet Campus (0.98 NTU) is lower than the WHO recommended value of 5.00 NTU.

10. Potability:

Indicates if water is safe for human consumption where 1 means Potable and 0 means Not potable.

Importing Libraries

Loading the Dataset

Resampling the Data

Dealing with Null Values

Since there are many null values and approx. 3000 input values only, so removing null values would not be beneficial for us, instead I replaced the null values with the mean.

Correlation in Data using Heatmap

Data is not correlated with each other as we can see in the Heatmap

Normalising the Data

Splitting the Data

Building and Fitting the Model

Plotting Accuracy and Loss

Saving the Model and deepCC

Conclusion

This is one of the many ways to predict whether the water is potable(drinkable) or not.

Link to Notebook — https://cainvas.ai-tech.systems/notebooks/details/?path=tanmay/water_potability_test.ipynb

Water Potability Test was originally published in AITS Journal on Medium, where people are continuing the conversation by highlighting and responding to this story.