Please help me solve file DAC's Classification and Evaluation according to the pictured instructions.

#Step $1$:

# Import libraries

# In this section, you can use a search engine to look for the functions that will help you implement the following steps

#Step $2$:

# Load dataset and show basic statistics

# $1.$ Show dataset size $($dimensions$)$

# $2.$ Show what column names exist for the $49$ attributes in the dataset

# $3.$ Show the distribution of the target class CES $4\mathrm{.}0$ Percentile Range column

# $4.$ Show the percentage distribution of the target class CES $4\mathrm{.}0$ Percentile Range column

# Step $3$:

#Clean the dataset $-$ you can eitherhandle the missing values in the dataset

# with the mean of the columns attributes or remove rows the have missing values.

# Step $4$:

#Encode the Categorical Variables $-$ Using OrdinalEncoder from the category$\_$encoders library to encode categorical variables as ordinal integers

# Step $5$:

# Separate predictor variables from the target variable $($attributes $($X$)$ and target variable $($y$)$ as we did in the class$)$

# Create train and test splits for model development. Use the $90\%$ and $20\%$ split ratio

# Use stratifying $($stratify$=$y$)$ to ensure class balance in train$/$test splits

# Name them as X$\_$train, X$\_$test, y$\_$train, and y$\_$test

# Name them as X$\_$train, X$\_$test, y$\_$train, and y$\_$test

X$\_$train $=[]$ # Remove this line after implementing train test split

X$\_$test $=[]$ # Remove this line after implementing train test split

# Do not do steps $6-8$ for the Ramdom Forest Model

# Step $6$:

# Standardize the features $($Import StandardScaler here$)$

# Step $7$:

# Below is the code to convert X$\_$train and X$\_$test into data frames for the next steps

cols $=$ X$\_$train.columns

X$\_$train $=$ pd$.$DataFrame$($X$\_$train, columns$=[$cols$])$ # pd is the imported pandas lirary $-$ Import pandas as pd

X$\_$test $=$ pd$.$DataFrame$($X$\_$test, columns$=[$cols$])$ # pd is the imported pandas lirary $-$ Import pandas as pd

# Step $8-$ Build and train the SVM classifier

# Train SVM with the following parameters. $($use the parameters with the highest accuracy for the model$)$

$$# $1.$ RBF kernel

$$# $2.$ C$=10\mathrm{.}0($Higher value of C means fewer outliers$)$

$$# $3.$ gamma $=0\mathrm{.}3($Linear$)$

# Test the above developed SVC on unseen pulsar dataset samples

# compute and print accuracy score

# Save your SVC model $($whatever name you have given your model$)$ as $.$sav to upload with your submission

# You can use the library pickle to save and load your model for this assignment

# Optional: You can print test results of your model here if you want. Otherwise implement them in evaluation.py file

# Get and print confusion matrix

cm $=[[]]$

# Below are the metrics for computing classification accuracy, precision, recall and specificity

TP $=$ cm$[0,0]$

TN $=$ cm$[1,1]$

FP $=$ cm$[0,1]$

FN $=$ cm$[1,0]$

# Compute Precision and use the following line to print it

precision $=0$ # Change this line to implement Precision formula

print$(\text{'}$Precision : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($precision$))$

# Compute Recall and use the following line to print it

recall $=0$ # Change this line to implement Recall formula

print$(\text{'}$Recall or Sensitivity : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($recall$))$

# Compute Specificity and use the following line to print it

specificity $=0$ # Change this line to implement Specificity formula

print$(\text{'}$Specificity : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($specificity$))$

# Step $9$: Build and train the Random Forest classifier

# Train Random Forest $$with the following parameters.

# $($n$\_$estimators$=10,$ random$\_$state$=0)$

# Test the above developed Random Forest model on unseen DACs dataset samples

# compute and print accuracy score

# Save your Random Forest model $($whatever name you have given your model$)$ as $.$sav to upload with your submission

# You can use the library pickle to save and load your model for this assignment

# Optional: You can print test results of your model here if you want. Otherwise implement them in evaluation.py file

# Get and print confusion matrix

cm $=[[]]$

# Below are the metrics for computing classification accuracy, precision, recall and specificity

TP $=$ cm$[0,0]$

TN $=$ cm$[1,1]$

FP $=$ cm$[0,1]$

FN $=$ cm$[1,0]$

# Compute Classification Accuracy and use the following line to print it

classification$\_$accuracy $=0$

print$(\text{'}$Classification accuracy : $\{0$:$0\mathrm{.}4$f$\}\text{'}.$format$($classification$\_$accuracy$))$

# Compute Precision and use the following line to print it

precision $=0$ # Change this line to implement Precision formula

print$(\text{'}$Precision : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($precision$))$

# Compute Recall and use the following line to print it

recall $=0$ # Change this line to implement Recall formula

print$(\text{'}$Recall or Sensitivity : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($recall$))$

# Compute Specificity and use the following line to print it

specificity $=0$ # Change this line to implement Specificity formula

print$(\text{'}$Specificity : $\{0$:$0\mathrm{.}3$f$\}\text{'}.$format$($specificity$))$

Evaluation file in picture

Assignment brief in picture