AI principles

AI engineer training

Name 4 eda(Explore Data Libraries)

import pandas as pd

import numpy as np

%matplotlib inline

import matplotlib.pyplot as plt

import seaborn as sns

Name 6 Models from Scikit-Learn (estimators/models)

from sklearn.linear_model import LogisticRegression

from sklearn.neighbors import KNeighborsClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.pipeline import make_pipeline

from sklearn import preprocessing

from sklearn.preprocessing import StandardScaler

Name 5 Model Evaluators used split the data, tune the data and get your classificatin metrics

# split data into training and test sets

from sklearn.model_selection import train_test_split, cross_val_score

from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

from sklearn.metrics import confusion_matrix, classification_report

# classification metrics

from sklearn.metrics import precision_score, recall_score, f1_score

from sklearn.metrics import RocCurveDisplay

Give an example of how to import and show data from a file using Pandas

dataframe = pd.read_csv("data/heart-disease.csv")

dataframe.head(1)