<jupyter_start><jupyter_text>IRIS DATASET  Problem Statement:-The target variable comprises of 3 different species of iris flower. Our motive is to classify the three different classes with the help of given features using Machine Learning Algorithms.  Dataset Information:-This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other.Predicted attribute: class of iris plant.This is an exceedingly simple domain.This data differs from the data presented in Fishers article (identified by <NAME>, spchadwick '@' espeedaz.net ). The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.  Attribute Information:-1. sepal length in cm2. sepal width in cm3. petal length in cm4. petal width in cm5. class:-- Iris Setosa-- Iris Versicolour-- Iris Virginica  We will be implementing the following steps to achieve the final  result:-1. Importing the necessary Libraries.2. Importing the Dataset.3. Exploratory Data Analysis.4. Dividing the data into test and train datasets.5. Building the model.6. Evaluating the performance.  Step-1: Importing the Libraries:-<jupyter_code># import numpy as np for processing data


# import pandas as pd for importing the data and working with data


# import matplotlib.pyplot as plt for visualisation


# import seaborn as sns for data visualization<jupyter_output><empty_output><jupyter_text>Step-2: Importing the dataset:-<jupyter_code># Using pd.read_csv(filename) we can import the dataset<jupyter_output><empty_output><jupyter_text>Seeing the dataset:-<jupyter_code># Using data.head() we can see the dataset<jupyter_output><empty_output><jupyter_text>Step-3: Exploratory Data Analysis:-It is common for data scientists to spend a majority of their time exploring and cleaning data, but approaching this as an opportunity to invest in your model (instead of viewing it as just another chore on your to-do list) will yield big dividends later on in the data science process.Performing thorough exploratory data analysis (EDA) and cleaning the dataset are not only essential steps, but also a great opportunity to lay the foundation for a strong machine learning model.  Seeing the shape and size of dataset:-<jupyter_code># Using data.shape and data.size we can see the shape and size of dataset<jupyter_output><empty_output><jupyter_text>Describing the dataset:-<jupyter_code># Using data.describe() we will be describing the dataset<jupyter_output><empty_output><jupyter_text>Seeing the non-null values:-<jupyter_code># Using data.info() we can see the non-null values in the dataset<jupyter_output><empty_output><jupyter_text>Seeing the list of columns:-<jupyter_code># Using data.columns we can see the list of all the columns in the dataset<jupyter_output><empty_output><jupyter_text>Seeing the null values in the dataset:-<jupyter_code># Using data.isnull().sum() we can see the null values in the dataset<jupyter_output><empty_output><jupyter_text>Visualising the data using seaborn library:-There are different types of plots like bar plot, box plot, scatter plot etc.Scatter plot is very useful when we are analyzing the relation ship between 2 features on x and y axis.In seaborn library we have pairplot function which is very useful to scatter plot all the features at once instead of plotting them individually.<jupyter_code># Using sns.pairplot(data) we can visualise the relationship between the features<jupyter_output><empty_output><jupyter_text>Visualising the Heatmap:-<jupyter_code># Using sns.heatmap(data.corr(),annot=True) we can visualise the heatmap<jupyter_output><empty_output><jupyter_text>Visualising the Violin Map:-<jupyter_code># Using sns.violinplot(x='species',y='sepal length (cm)',data=data)<jupyter_output><empty_output><jupyter_text>Breaking the dataset into feature and target variables:-<jupyter_code># Using data.iloc[:,:-1].values we will get our features

# Using data.iloc[:,-1].values we will get our target variable<jupyter_output><empty_output><jupyter_text>Dividing the data into train and test data:-<jupyter_code># Using train_test_split() we can divide the data into training data and testing data<jupyter_output><empty_output><jupyter_text>Training the model with Decision Tree as base estimator:-<jupyter_code># First we will be importing AdaBoostClassifier from sklearn.ensemble

# Building the model

# Fitting the model on training data

# Predicting the test values using the model<jupyter_output><empty_output><jupyter_text>Evaluating the model:-<jupyter_code># Using confusion_matrix and accuracy score we will be evaluating the model's performance<jupyter_output><empty_output><jupyter_text>Training the model with SVC as base estiamtor:-<jupyter_code># First we will be importing theb AdaBoostClassifier

# Now we will import SVC

# Now using svc as base estimator we will be building our model

# Fitting the model on our training dataset

# Predicting the test values using the model<jupyter_output><empty_output><jupyter_text>Evaluating the model's performance:-<jupyter_code># Using confusion_matrix and accuracy score we will be evaluating the model's performance<jupyter_output><empty_output>
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