Skip to main content

ML APP | FISH SPECIES PREDICTION


In this article we are going to visualize and predict the fish species using various illustrations and python libraries. We have downloaded  the dataset for this particular web app  from kaggle.com. This web app is built with python streamlit library. We have also included exploratory data analysis and data visualization.

Machine learning web application output

The dataset has seven columns namely Species, Weight, Length1, Length2, Lenght3, Height, Width of the fish. In this Machine learning web app we are predicting the fish Species based on other six attributes. This dataset include seven most common fish species namely Bream, Perch, Pike, Whitefish, Roach, Smelt, Parkki. We have trained our machine learning model using this data to predict the fish species on the new data. On the home page of the app user needs to enter all the input attributes and click on predict button to find out the fish species. The result obtained is the image of fish species and the type of species.

 User can navigate to Home, EDA or ABOUT section rom the navigation side bar. The EDA section has EXPLORATORY DATA ANALYSIS and DATA VISUALIZATION sub-sections. Clicking on Show dataset checkbox shows the dataset used for this web app. Also we have show columns , Show shape, Show summery, and correlation check box. 

Streamlit python EDA


Libraries used for data visualization are matplotlib, seaborn and PIL for displaying images in the app.

The DATA VISUALIZATION section has simple value plot, scatter plot and box plot checkboxes. Simple value plot gives count of each species in the dataset. We have used scatterplot for this dataset to check for any outliers in the dataset. Analyzing the outliers we have filtered out the outliers to increase the performance of our model.  You can also see the boxplot for the species column by clicking on Box Plot in the app.

data visualization in streamlit python

The details about this app and how to use it, is given in the ABOUT section. The brief about the input attributes is also provided in this section.



Labels:

Comments

Post a Comment

Popular posts from this blog

Salary Prediction Web App using Streamlit

Salary Prediction Web App In this article, we are going to discuss how to predict the salary based on various attributes related to salary  using Random Forest Regression. This study focuses on a system that predicts the salary of a candidate based on candidate’s qualifications, historical data, and work experience. This app uses a machine learning algorithm to give the result. The algorithm used is Random Forest Regression. In this problem, the target variable (or output), y, takes value of salary for a given set of input features (or inputs), X. The dataset contains gender, secondary school percentage, higher secondary school percentage, higher secondary school stream, degree percentage, degree type, work experience and specialization of candidate. Below is the step-by-step Approach: Step 1: Import the necessary modules and read the dataset we are going to use for this analysis. Below is a screenshot of the dataset we used in our analysis. Step 2: Now before moving ...
Post a Comment
Read more

STREAMLIT MULTIPAGE WEB APPLICATION | AREA CALCULATOR

Multipage Web App So far, we have worked with python streamlit library and we have built machine learning web applications using streamlit. In this blog we will see how to build a multi-page web app using streamlit. Streamlit multipage web app We can create multiple apps and navigate across each of them in a main app using a radio button. First, we have created separate apps for each shape to calculate the area of that particular shape example app1.py, app2.py, app3.py etc. Then we have created a main app and added a navigator using radio buttons. Now we just have to run the main app and navigate through the desired web page. Area Calculator This particular multipage web app we named it as area calculator. We have included introduction page and ten shapes of which we can calculate the area by putting required inputs. We have downloaded the multiapp.py framework from GitHub, as we have a greater number of web pages. Each shape in the navigation bar indicates new web p...
Post a Comment
Read more