Goals of ML Problem ?

The goal of any machine learning problem is to find a single model that will best predict our wanted outcome. Rather than making one model and hoping this model is the best/most accurate predictor we can make, ensemble methods take a myriad of models into account, and average those models to produce one final model. It is important to note that Decision Trees are not the only form of ensemble methods, just the most popular and relevant in DataScience today.

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CondaValueError: Value error: invalid package specification

Recently I was trying to create Conda Environment and wanted to install Tensorflow but i have faced some issue , so i have done some research and done trouble shooting related to that . Here am going to share how to trouble shoot if you are getting Conda Value error while creating Conda environment and install tensorflow . Open Anaconda Prompt (as administrator if it was installed for all users) Run conda update conda Run the installer again Make sure all pkg are updated: Launch the console from Anaconda Navigator and conda create -n mypython python=3.6.8 After Installing Conda environment please active the conda now : conda activate mypython once conda environment has been activated kindly install tensorflow 2.0 by using this command pip install tensorflow==2.0.0 once Tensorflow has been successfully install kindly run the command : pip show tensorflow Try to Run Comman PIP Install Jupyter lab and after ins...

Important Python Libraries for Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries. Important Python Libraries for Data Science: Pandas NumPy SciPy Matplotlib TensorFlow Seaborn Scikit Learn Keras 1. Pandas Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitH...

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc. Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...

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