Explore the Thrills of Tennis M15 Ystad Sweden
Welcome to the ultimate hub for all things related to the Tennis M15 Ystad Sweden. Here, you will find daily updates on fresh matches, expert betting predictions, and in-depth analysis of every game. Whether you're a seasoned tennis enthusiast or new to the sport, this is your go-to destination for all the latest information and insights.
Why Follow Tennis M15 Ystad Sweden?
The Tennis M15 Ystad Sweden tournament is a vibrant and competitive event that attracts top young talents from around the globe. With its dynamic matches and promising athletes, it's an exciting platform for players to showcase their skills and for fans to discover future stars of the sport.
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- Emerging Talent: Get to know the next generation of tennis stars.
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Daily Match Updates
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Understanding the nuances of each match can enhance your viewing experience and betting strategy. Our in-depth analyses cover every aspect of the game, providing you with valuable insights into player tactics, strengths, and weaknesses.
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Meet the Players
The Tennis M15 Ystad Sweden tournament is a showcase for some of the most promising young players in the sport. Get to know these rising stars through detailed player profiles and interviews.
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The Venue: Ystad Sweden
The picturesque town of Ystad in Sweden serves as the perfect backdrop for this prestigious tournament. Known for its beautiful landscapes and vibrant culture, Ystad offers a unique experience for both players and spectators.
- Venue Highlights: Explore the facilities and amenities available at the tournament site.
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The Future of Tennis: Insights from M15 Ystad Sweden
kacperkleban/Statistics-with-Python<|file_sep|>/README.md
# Statistics-with-Python
My course work during my master degree studies
## Linear regression
In this assignment I used Python language (with numpy, pandas, matplotlib.pyplot) to predict house prices using linear regression algorithm.
The dataset consists of information about house prices (medv) as well as some features like number of rooms (rm), crime rate (crim), pollution index (nox), distance from employment centers (dis) etc.
I started with analyzing data:
- checking missing values
- computing descriptive statistics
- visualizing distributions
Then I divided data into training (80%) and testing sets (20%). I also standardized features.
In order to find best set of features I used forward selection method. This method starts with empty set of features then adds one feature at a time which improves model fit most.
Next step was fitting linear regression model using selected features. In order to find best fit I used cross-validation method.
I also computed adjusted R-squared value which indicates how well linear regression model fits data.
At last I computed mean squared error on test set.
## Logistic regression
In this assignment I used Python language (with numpy, pandas, matplotlib.pyplot) to predict whether person has diabetes or not using logistic regression algorithm.
The dataset consists of information about persons who have or haven't diabetes as well as some features like blood pressure (bp), body mass index (bmi), age etc.
I started with analyzing data:
- checking missing values
- computing descriptive statistics
- visualizing distributions
Then I divided data into training (80%) and testing sets (20%). I also standardized features.
I fitted logistic regression model using training set. In order to find best fit I used cross-validation method.
At last I computed mean squared error on test set.
<|repo_name|>kacperkleban/Statistics-with-Python<|file_sep|>/Assignment_1.py
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 29 13:53:02 2018
@author: Kacper Kleban
Assignment #1 - Linear Regression
Data file - Boston housing data
https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
In this assignment I will use Python language (with numpy, pandas,
matplotlib.pyplot) to predict house prices using linear regression algorithm.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("Boston_housing.csv")
df.head()
# Checking missing values:
print(df.isnull().sum())
# Computing descriptive statistics:
print(df.describe())
# Visualizing distributions:
fig = plt.figure(figsize=(12,12))
ax1 = fig.add_subplot(331)
ax1.hist(df['crim'])
ax1.set_xlabel('Crime rate')
ax1.set_ylabel('Frequency')
ax1.set_title('Distribution of crime rate')
ax2 = fig.add_subplot(332)
ax2.hist(df['zn'])
ax2.set_xlabel('% residential land zoned for lots over')
ax2.set_ylabel('Frequency')
ax2.set_title('Distribution % residential land zoned for lots over')
ax3 = fig.add_subplot(333)
ax3.hist(df['indus'])
ax3.set_xlabel('% non-retail business acres per town')
ax3.set_ylabel('Frequency')
ax3.set_title('Distribution % non-retail business acres per town')
ax4 = fig.add_subplot(334)
ax4.hist(df['chas'])
ax4.set_xlabel('Charles River dummy variable (=1 if tract bounds river)')
ax4.set_ylabel('Frequency')
ax4.set_title('Distribution Charles River dummy variable')
ax5 = fig.add_subplot(335)
ax5.hist(df['nox'])
ax5.set_xlabel('Nitric oxides concentration (parts per ten million)')
ax5.set_ylabel('Frequency')
ax5.set_title('Distribution Nitric oxides concentration')
ax6 = fig.add_subplot(336)
ax6.hist(df['rm'])
ax6.set_xlabel('Average number of rooms per dwelling')
ax6.set_ylabel('Frequency')
ax6.set_title('Distribution Average number of rooms per dwelling')
ax7 = fig.add_subplot(337)
ax7.hist(df['age'])
ax7.set_xlabel('% owner occupied units built prior to 1940')
ax7.set_ylabel('Frequency')
ax7.set_title('Distribution % owner occupied units built prior to')
plt.show()
# Splitting data into training set (80%) and testing set (20%):
np.random.seed(1)
perm = np.random.permutation(len(df))
train_set = perm[:int(len(df)*0.8)]
test_set = perm[int(len(df)*0.8):]
X_train = df.iloc[train_set].drop(['medv'], axis=1).values
y_train = df.iloc[train_set]['medv'].values.reshape(-1,1)
X_test = df.iloc[test_set].drop(['medv'], axis=1).values
y_test = df.iloc[test_set]['medv'].values.reshape(-1,1)
# Standardizing features:
from sklearn.preprocessing import StandardScaler
scaler_X = StandardScaler()
scaler_y = StandardScaler()
X_train_std = scaler_X.fit_transform(X_train)
y_train_std = scaler_y.fit_transform(y_train)
X_test_std = scaler_X.transform(X_test)
y_test_std = scaler_y.transform(y_test)
# Forward selection method:
def RSS(y_true,y_pred):
return np.sum((y_true-y_pred)**2)
def R_squared(y_true,y_pred):
return np.sum((y_pred-y_true.mean())**2)/np.sum((y_true-y_true.mean())**2)
def Adjusted_R_squared(R_squared,k,n):
return R_squared-(k-1)/(n-k)*(1-R_squared)
def forward_selection(X,y):
k=0 # number of features included in model
best_features=[] # list containing indices of features included in model
RSS_list=[RSS(y,np.ones(len(y)))] # list containing RSS value after adding each feature
R_squared_list=[0] # list containing R-squared value after adding each feature
Adjusted_R_squared_list=[0] # list containing adjusted R-squared value after adding each feature
n=len(y) # number of observations
while k# -*- coding: utf-8 -*-
"""
Created on Mon Apr 23 12:14:42 2018
@author: Kacper Kleban
Assignment #3 - Logistic Regression
Data file - Diabetes dataset
https://www.kaggle.com/uciml/pima-indians-diabetes-database/downloads/pima-indians-diabetes-database.zip/3
In this assignment I will use Python language (with numpy, pandas,
matplotlib.pyplot) to predict whether person has diabetes or not using
logistic regression algorithm.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df=pd.read_csv("diabetes.csv")
# Checking missing values:
print(df.isnull().sum())
# Computing descriptive statistics:
print(df.describe())
# Visualizing distributions:
fig=plt.figure(figsize=(12,12))
plt.suptitle("Distribution", fontsize=16)
plt.subplot(331)
plt.hist(df['Pregnancies'], bins=10)
plt.xlabel("Number of pregnancies")
plt.ylabel("Frequency")
plt.subplot(332)
plt.hist(df['Glucose'], bins=10)
plt.xlabel("Plasma glucose concentration")
plt.ylabel("Frequency")
plt.subplot(333)
plt.hist(df['BloodPressure'], bins=10)
plt.xlabel("Diastolic blood pressure")
plt.ylabel("Frequency")
plt.subplot(334)
plt.hist(df['SkinThickness'], bins=10)
plt.xlabel("Triceps skin fold thickness")
plt.ylabel("Frequency")
plt.subplot(335)
plt.hist(df['Insulin'], bins=10)
plt.xlabel("Serum insulin concentration")
plt.ylabel("Frequency")
plt.subplot(336)
plt.hist(df['BMI'], bins=10)
plt.xlabel("Body mass index")
plt.ylabel("Frequency")
plt.subplot(337)
plt.hist(df['DiabetesPedigreeFunction'], bins=10)
plt.xlabel("Diabetes pedigree function")
plt.ylabel("Frequency")
plt.subplot(338)
plt.hist(df['Age'], bins=10)
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.subplot(339)
df.groupby(["Outcome"]).size().plot(kind="bar", color=['blue','orange'])
locs,ticks=plt.xticks()
labels=["No diabetes","Diabetes"]
for loc,label in zip(locs,labels):
plt.text(loc,label+" ("+str(int(ticks[loc]))+")",ha='center',va='bottom')
fig.tight_layout(rect=[0,0.03,1,.95])
#plt.show()
# Splitting data into training set (80%) and testing set (20%):
np.random.seed(12345)
perm=np.random.permutation(len(df))
train_set=perm[:int(len(df)*0.8)]
test_set=perm[int(len(df)*0.8):]
X_train=df.iloc[train_set].drop(["Outcome"],axis=1).values
y_train=df.iloc[train_set]["Outcome"].values.reshape(-1,1)
X_test=df.iloc[test_set].drop(["Outcome"],axis=1).values
y_test=df.iloc[test_set]["Outcome"].values.reshape(-1,1)
# Standardizing features:
from sklearn.preprocessing import StandardScaler
scaler_X=StandardScaler()
scaler_y=StandardScaler()
X_train_std=scaler_X.fit_transform(X_train)
y_train_std=scaler_y.fit_transform(y_train)
X_test_std=scaler_X.transform(X_test)
y_test_std=scaler_y.transform(y_test)
# Fitting logistic regression model:
from sklearn.linear_model import LogisticRegressionCV
log_reg_cv=LogisticRegressionCV(cv=10,solver='lbfgs',max_iter=10000)
log_reg_cv.fit(X_train_std,y_train_std.ravel())
y_pred=log_reg_cv.predict(X_test_std)
from sklearn.metrics import accuracy_score
accuracy=accuracy_score(y_test_std,y_pred)
print("Accuracy:",accuracy)<|repo_name|>kacperkleban/Statistics-with-Python<|file_sep|>/Assignment_4.py
# -*- coding: utf-8 -*-
"""
Created on Tue May 22 11:49:44 2018
@author: Kacper Kleban
Assignment #4 - Naive Bayes Classifier
Data file - Diabetes dataset
https://www.kaggle.com/uciml/pima-indians-diabetes-database/downloads/pima-indians-diabetes-database.zip/3
In this assignment I will use Python language (with numpy,
pandas,mpl_toolkits.mplot3d,Axes3D) to classify whether person has diabetes or not
using naive Bayes classifier.
"""
import numpy as np
import pandas as pd
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
df=pd.read_csv("diabetes.csv")
df.head()
# Checking missing values:
print(df.isnull().sum())
# Computing descriptive statistics:
print(df.describe())
# Visualizing distributions:
fig=plt.figure(figsize=(12,12))
fig.suptitle("Distribution", fontsize=16)
plt.subplot(331)
df.groupby(["Pregnancies"]).size().plot(kind="bar", color='blue')
locs,ticks=plt.xticks()
for loc,label in zip(locs,ticks):
plt.text(loc,label+" ("+str(int(ticks[loc]))+")",ha='center',
