In this blog following topics will be covered
- A Basic Approach To Solving A Problem Using Data Science
- Practical Implementation of Data Science
- Data Science Projects
- How Do You Solve Data Science Problems?
A Basic Approach To Solving A Problem Using Data Science
- With accurate data, data science can be used to solve problems ranging from fraud detection and smart farming to climate change and the prognosis of heart disease.
- That being said, the data is not enough to solve a problem, you need an approach or method that will give you the most accurate results.
Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technologies.
How Do You Solve Data Science Problems?
A problem statement in data science can be solved by following these steps:
- Define problem report / business requirement
- Data collection
- Data cleaning
- Data analysis and analysis
- Data modeling
- Deployment and optimization
Step 1: Define the problem statement
- Before you start a data science project, you need to define the problem you are trying to solve.
- At this point, you need to be clear with the goals of your plan.
Step 2: Data collection
- At this point you will get all the data you need to fix the problem as the name implies.
- Collecting data is not easy because you can not see the data sitting in the database, waiting for you.
- Instead, you should go out and do some research and collect data or clear the internet.
Step 3: Data Cleaning
- If you ask a data scientist what their favorite process is in data science, they are going to tell you that it is mostly data cleaning.
- Data cleaning is the process of removing unwanted, missing, copied and unwanted data.
- This stage is one of the most time consuming stages in data science. However, to avoid miscalculations, it is important to avoid inconsistencies in the data.
Step 4: Data analysis and Exploration
- At this stage in the Data science life cycle, you need to discover the shapes and trends of data.
- You are here to retrieve useful statistics and study the nature of the data.
- At the end of this step, you should start making assumptions about your data and the problem you are dealing with.
Step 5: Data modeling
- This level is to create a model that best solves your problem.
- A model is a machine learning tool that is trained and tested using data.
- This step always starts with a process called data splitting, where you divide your entire data into two ratios.
- One is to train the model (training data set) and the other is to test the performance of the model (experimental data set).
Step 6: Optimization and Deployment:
- This is the final stage of the data science life cycle.
- At this point, you should try to improve the performance of the data model so that you can make more accurate predictions.
- The ultimate goal is to use the model adopted by the end user in the production or production environment.
- Users should check the performance of the models and fix any issues with the model at this point.
Practical Implementation of Data Science
Problem Statement:
To build a model that will predict whether a person has diabetes based on patient information such as blood pressure, body mass index (BMI), and age. Tutorial on various stages of data science . In particular, the blog article includes the following sections
- Overview
- Data Interpretation
- Data analysis
- Data production
- 5Training and evaluation of the machine learning model
- 6Explaining the ML model
- Saving the sample
- Making predictions with the model
Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technologies.
Overview
- The data was collected by the National Institute of Diabetes and Digestive and Kidney Diseases as part of the Pima Indians Diabetes Database.
- Many restrictions were placed on selecting these events from a large database. In particular, all patients here belong to the
- Pima Indians Diabetes Database are women 21 years of age and older.
Data Interpretation
- Our data is stored in a CSV file called Diabetes CSV.
- We first read our database in the Pandas dataframe called Diabetes Data Frame, and then display the first five entries from our database using the head () function
We are provided with the following features to predict whether a person is diabetic or not:
- Glucose: Plasma glucose concentration above 2 hours in oral glucose tolerance test
- Blood pressure: Diastolic blood pressure (mm Hg)
- Skin Thickness: Triceps skin fold thickness (mm)
- Insulin: 2-hour serum insulin (mu U / ml)
- BMI: body mass index (kg weight / (m height) 2)
- Diabetes function: Diabetes hereditary function (a function of assessing the risk of diabetes based on family history)
- Age: Age (years)
- Outcome: Class variable (0 if diabetic, 1 if diabetic)
Data Science Projects
Character Recognition
- The project focuses on the ability of a computer to recognize and understand human handwriting characters.
- Training is provided using a winding neural network MNIST database.
- This allows the neural network to recognize handwriting numbers with reasonable accuracy.
- This program utilizes Deep learning and requires Keras libraries.
Diagnosing breast cancer
- The breast cancer diagnostic program uses histology images to classify whether or not a patient has invasive ductal carcinoma.
- The program uses an IDC database to classify histology images as malignant or benign.
- A convolution neural network is best suited for this task.
- This model is trained using about 80% of the database and the rest of the database is used to test the accuracy of the sample after training.
Impact of climate change on global food supply
- Climate change and paradoxes are becoming a common part of our world these days.
- It is beginning to affect all areas of human life on our planet.
- The project focuses on measuring the impact of climate change and global food production.
- The objective of this project is to assess the impact of climate change on major crop production.
- The project assesses the effect of carbon dioxide on plant growth and the uncertainty of climate change and climate change.
- This scheme deals with data visualization and drawn comparisons between yields at different times in different regions.
Chatbot
- Chats play an important role in businesses. They help to provide innovative and personalized services while saving manpower.
- Using a vocabulary and vocabulary with in-depth learning methods, a chatbot can train with a database containing a list of common
- phrases, the purpose behind them and the appropriate answers.
- The most common way to train chatbots is to use continuous neural networks (RNNs).
- The input sentence contains an encoder boat that mentally updates its states and sends the state to the boat.
- The boat decoder will find the appropriate answer for the words and the purpose behind them.
- You can easily activate the chatbot with Python.
Detection of fake news
- The idea behind this project is to create a machine learning model that can detect whether the messages provided by any social media post are true or not.
- You can use TfidfVectorizer and PassiveAggressive classifier to create this model.
- TF or frequency is the number of times a word appears in a document.
- IDF or inverse file frequency is the number of times a word occurs in different documents. Many documents do not give much meaning to common words.
- TFIDFVectorizer analyzes the set of documents and creates the TF-IDF matrix accordingly
Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technologies.
