BANASTHALI VIDYAPITH
Artificial Intelligence and Machine Learning
LAB RECORD
SUBMITTED TO:- DR. URVASHI PRAKASH SHUKLA
SUBMITTED BY:- MANSI SINGHAL
ROLL NO. :- 2016776
CLASS:- B.TECH (IT-A)
SMART ID:- BTBTI20050
PROGRAM # 1
AIM:- Write a Python programme that uses a for loop to print prime numbers
between 1 to 1000.
CODE:-
OUTPUT :-
CONCLUSION:- For loops are used when we have a block of code which we want
to repeat a fixed number of times. To loop through a set of code a specified number
of times, we can use the range() function.
PROGRAM # 2
AIM:- Write a while loop-based Python programme to print the even values
between 4000 and 10000.
CODE:-
OUTPUT:-
CONCLUSION :- In while loop we can execute a set of statements as long as a
condition is true. Always remember to increment ‘i’, or else the loop will continue
forever.
PROGRAM # 3
AIM :– Write a programme that uses a single function to calculate the area of a
specific shapes.
CODE :–
OUTPUT :–
LAB - 2
PROGRAM # 1
AIM :- Program to learn preprocessing via sklearn via minMax Scaler.
Step 1 :- Import preprocessing and StandardScaler.
Step 2 :- Standardize the values of variables into a standard format using
“MinMaxScaler.”
OUTPUT :-
CONCLUSION :– Transform features by scaling each feature to a given range.This
estimator scales and translates each feature individually such that it is in the given
range on the training set, e.g. between zero and one.
LAB - 3
PROGRAM # 1
AIM :– Program to learn Python Basics.
STEP 1 :- Print any statement.
OUTPUT:-
STEP 2:- if statement
OUTPUT:-
STEP 3:- Type Casting
OUTPUT :-
Step 4:- Assigning Multiple Values
OUTPUT :-
Step 5:- Function
OUTPUT :-
Step 6 :- Data Types
OUTPUT :-
Step 7 :- Array
OUTPUT :-
LAB - 4
PROGRAM # 1
AIM :– To pre-process data to fit in the machine learning model.
STEP 1 :- Pyplot
OUTPUT :-
CODE :-
OUTPUT :-
CODE :-
OUTPUT :-
CODE :-
OUTPUT :-
Step 2 :- Histogram
CODE :–
OUTPUT :-
CONCLUSION :– Matplotlib is a cross-platform, data visualization and graphical
plotting library for Python and its numerical extension NumPy.
PROGRAM # 2
AIM :– Program to read a CSV File Format using “pandas Library”.
CODE :–
OUTPUT :–
CONCLUSION :– It is very easy and simple to read a CSV file using pandas library
functions. Here the read_csv() method of the pandas library is used to read data
from CSV files. We must import the Pandas library. read_csv() that retrieves data in
the form of the Dataframe.
PROGRAM # 3
AIM :– Program to check for “NaN” value in Pandas DataFrame
CODE :–
OUTPUT :–
PROGRAM # 4
AIM :– Program to fill the missing values.
Step 1 : Program to fill the missing glucose value with a constant value.
CODE :–
OUTPUT :–
CONCLUSION :- All the missing values in the price column are filled with the same
value that is 183.
STEP 2 :- Program to fill the missing glucose value with the median value of the
entire column.
CODE :–
OUTPUT :–
STEP 3 :- Program to fill the missing glucose value with the mean value of the
entire column.
CODE :–
OUTPUT :–
STEP 4 :- To fill the missing glucose values using “Forward Fill”..
CODE :–
OUTPUT :–
STEP 5 :- To fill the missing prices using “Interpolate”.
CODE :–
OUTPUT :–
STEP 6 :- Standardize the values of variables into a standard format using
“MinMaxScaler.”
CODE :–
OUTPUT :–
STEP 6 :- Convert to dataframe.
CODE :–
OUTPUT :–
LAB - 6
PROGRAM # 1
AIM :– Program to predict the value of one variable based on the value of another
variable using “Linear Regression” in an array.
STEP 1 :- Importing all the necessary libraries .
STEP 2 :- Passing values to x and y array.
STEP 3 :- Calculating mean.
OUTPUT :–
STEP 4 :- Calculating sample covariance and sample variance.
CODE :–
OUTPUT :–
STEP 5 :- Calculating slope and intercept
CODE :–
OUTPUT :–
STEP 6 :- Plot the given data points.
CODE :–
OUTPUT :–
STEP 7 :- Calculating y_pred.
CODE :–
OUTPUT :–
STEP 8 :- Plot the given data points and fit the regression line.
CODE :–
OUTPUT :–
STEP 9 :- Calculating Absolute squared error.
OUTPUT :–
STEP 10 :- Calculating mean squared error.
OUTPUT :–
STEP 11 :- Calculating root mean squared error.
OUTPUT :-
STEP 12 :- Calculating r square.
OUTPUT :-
STEP 13 :- Calculating adjusted r square.
OUTPUT :-
CONCLUSION :–
Linear Mean Root Mean R square Adjusted R Mean
Regression Squared squared square Absolute
Model Error error Error
Single 2.1600000000 1.469693845 0.878923766 - 1.2800000000
00001 6699071 8161435 0.484304932735 000007
Variable 4261
LAB - 7
PROGRAM # 1
AIM :– Program to predict the value of a Glucose based on the value of another
variable using “Linear Regression” in “Diabetes Dataset” and splitting the data for
training and testing purposes.
CODE –
STEP 1 :- Importing all the necessary libraries and loading the diabetes dataset.
OUTPUT :-
STEP 2 :- Converting the x and y column to the numpy array.
STEP 3 :- Reshape array in such a way that the resulting array has only 1 column.
STEP 4 :- Standardize the values of variables into a standard format using
“MinMaxScaler.”
“Normalizing X”
CODE:-
OUTPUT :–
“Normalizing Y”
CODE:-
OUTPUT :–
Step 5 :- Importing Library and constructing arrays
import matplotlib.pyplot as plt
Step 6 :- Calculating mean of both independent and dependent variable.
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression
OUTPUT :–
STEP 7 :- Calculating sample covariance and sample variance.
OUTPUT :–
STEP 8 :- Calculating slope and intercept.
Sxy = np.sum((x_norm-x_mean)*(y_norm-y_mean))
Sxx = np.sum(pow((x_norm-x_mean),2))
Sxy , Sxx
OUTPUT :–
b1 = Sxy/Sxx
b0 = y_mean-b1*x_mean
STEP 9 :– Plot the given data points.
CODE :–
OUTPUT :–
plt.scatter(x_norm,y_norm)
plt.xlabel('Insulin X')
STEP 10 :- Calculating y_pred. plt.ylabel('Glucose Y')
CODE :–
y_pred = b1 * x_norm + b0
OUTPUT :–
STEP 11 :- Plot the given data points and fit the regression line.
CODE :–
OUTPUT :–
plt.scatter(x_norm,y_norm,color = 'red')
plt.plot(x_norm,y_pred,color = 'green')
plt.xlabel('X')
plt.ylabel('y')
STEP 12 :- Calculating Mean Absolute error.
CODE :–
OUTPUT :–
STEP 13 :- Calculating mean squared error.
CODE :–
OUTPUT :–
print("MAE = ", mean_absolute_error(y_norm,y_pred))
STEP 14 :- Calculating root mean squared error.
CODE :–
OUTPUT :–
STEP 15 :- Calculating R square.
print("MSE = ", mean_squared_error(y_norm,y_pred))
CODE :–
OUTPUT :–
CONCLUSION :–
Linear Mean Squared Root Mean R square Absolute R
Regression Error squared square
Model error
Single 0.0252938 0.159040 0.08596897 0.0847757
Variable
PROGRAM 2
//Splitting The Data
STEP 1 :– Import the library
STEP 2 :-Splitting the data.s
CODE :–
STEP 3 :– Calculating mean.
CODE :–
from sklearn.model_selection import train_test_split
OUTPUT :–
x_train, x_test, y_train, y_test = train_test_split(
x_norm, y_norm, train_size=538, test_size=230, random_
STEP 4 :- Calculating sample covariance and sample variance.
xtr_mean = np.mean(x_train)
ytr_mean = np.mean(y_train)
print(xtr_mean)
print(ytr_mean)
OUTPUT :–
STEP 5 :- Calculating slope and intercept.
OUTPUT :–
STEP 6 :- Plot the given data points.
CODE :–
b1_t = Sxyt/Sxxt
b0_t = ytr_mean-b1*xtr_mean
print('slope b1 is ',b1_t)
print('intercept b0 is',b0_t)
OUTPUT :–
plt.scatter(x_train,y_train)
plt.xlabel('Insulin X')
plt.ylabel('Glucose Y')
STEP 7 :- Calculating y_pred.
CODE :–
OUTPUT :–
y_predt = b1_t * x_test + b0_t
STEP 8 :- Plot the given data points and fit the regression line.
y_predt
CODE :–
OUTPUT :–
STEP 9 :- Calculating Mean Absolute error.
CODE :–
OUTPUT :–
STEP 10 :- Calculating mean squared error.
CODE :–
OUTPUT :–
STEP 11 :- Calculating root mean squared error.
print("MAE = ", mean_absolute_error(y_test,y_predt))
CODE :–
OUTPUT :–
STEP 12 :- Calculating R square.
CODE :–
OUTPUT :–
CONCLUSION :–
Linear Mean Mean Squared Root Mean
print("RMSE R square
= ", np.sqrt(mean_squared_error(y_test,y_pre
Regression Absolute Error squared
Model Error error
Single 0.13295691502 0.02921714818 0.1709302436 0.0109610308
902732 042113 09553 20120121
Variable
print("R2 = ", r2_score(y_test,y_predt))
