Machine-Learning-with-Scikit-Learn-Python-3.x

Defination: Machine learning is the scientific study of algorithms and statistical models that computer systems use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. When applying machine learning to real-world data, there are a lot of steps involved in the process -- starting with collecting the data and ending with generating predictions.

Steps To We Have To Build Machine Learning Models:

• Step 1: Gather the data In industry, there are important considerations you need to take into account when building a dataset, such as target.
• Step 2: Prepare the data Deal with missing values and categorical data. (Feature engineering,Feature Selection,Feature Transformation).
• Step 3: Select a model There are a lot of different types of models. Which one should you select based on Your business problem?
• Step 4: Train the model Fit Regression and Classifiaction models to patterns in training data.
• Step 5: Evaluate the model Use a validation set to assess how well a trained model performs on unseen data.
• Step 6: Tune parameters Tune parameters to get better performance from XGBoost models.
• Step 7: Get predictions Generate predictions with a trained model

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. See the About us <https://scikit-learn.org/dev/about.html#authors>__ page

for a list of core contributors.

It is currently maintained by a team of volunteers.

Website: https://scikit-learn.org

Installation

Dependencies

scikit-learn requires:

• Python (>= 3.6)

• NumPy (>= 1.13.3)

• SciPy (>= 0.19.1)

• joblib (>= 0.11)

• Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.

• scikit-learn 0.23 and later require Python 3.6 or newer.

Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display") require Matplotlib (>= 2.1.1). For running the examples Matplotlib >= 2.1.1 is required. A few examples require scikit-image >= 0.13, a few examples require pandas >= 0.18.0, some examples require seaborn >= 0.9.0.

---

User installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip ::

pip install -U scikit-learn

or conda::

conda install scikit-learn

The documentation includes more detailed installation instructions <https://scikit-learn.org/stable/install.html>_.

Scholeaofai

---

References To Learn and Develop your Self:

• Python
• Data Science With Python
• Machine Learning with Python
• Deep learning With python
• Data Visulization
• Life Cycle of Data Science
• Statistics