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The 7 steps of any Machine Learning problem to answering questions Gathering Data Preparing the Data Choosing a Model Training Evaluation Hyperparameter Tuning Prediction   Data Gathering We will first gather data, in order to train our model we need data for example if we are predicting whether a drink is wine or beer, so […]

Data Analysis – Machine Learning Pandas (data preparation) Pandas help you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R. Practical real world data analysis, reading and writing data, data alignment, reshaping, slicing, fancy indexing, and subsetting, size mutability, merging and joining, Hierarchical axis indexing, Time series-functionality.See More: Pandas […]

 Prerequisite for fully working of Apache Spark(pyspark) with Jupyter i.e  How to integrate Jupyter notebook and pyspark?Step 1: – Download and Installed. Download and install Anaconda. (Anaconda comes with lots of packages like Jupyter, ipython, python3 and many more so no need to install these packages explicitly) Download and install if not installed Java(Because spark uses JVM to run.) to […]

In our day-to-day life we generate a lot of data like tweets, facebook posts, comments, Blog posts, articles which are generally in our natural language and which  falls in category of semi-structured  and unstructured data, So as when we process natural language data “the unstructured data – plain text”  we call it Natural Language Processing. Natural […]

iPython Notebooks are the best way to showcase your Analysis, with the help of ipython notebooks you can tell stories with your code by embedding different types of visualizations, images and text. These iPython Notebooks are the simplest way to share you whole code history with your team-mates just like a blog.As the name suggest iPython is […]