Welcome to the Ultimate Serie B Italy Football Experience
Immerse yourself in the thrilling world of Serie B Italy football with our comprehensive platform. Every day, we bring you the freshest matches, complete with expert betting predictions to enhance your viewing experience. Whether you're a die-hard fan or a casual observer, our content is designed to keep you informed and engaged with every goal, tackle, and save. Let's dive into the excitement of Serie B Italy football together!
Understanding Serie B Italy Football
Serie B Italy, often referred to as the "Championship," is the second-highest division in Italian football. It serves as a crucial stepping stone for clubs aspiring to reach the prestigious Serie A. The league is renowned for its competitive spirit and has been the launchpad for many players who later become stars in Serie A and beyond. With 20 teams battling it out each season, Serie B offers a dynamic and unpredictable footballing spectacle.
Daily Match Updates: Stay Informed Every Day
Our platform ensures that you never miss a beat in Serie B Italy football. We provide daily updates on all matches, including scores, highlights, and key moments. Our dedicated team of analysts and reporters delivers in-depth coverage, ensuring you have all the information at your fingertips.
- Live Scores: Get real-time updates on match scores and results.
- Match Highlights: Watch replays of the most exciting goals and moments.
- Post-Match Analysis: Gain insights from expert commentary and analysis.
Expert Betting Predictions: Enhance Your Viewing Experience
Betting on football can add an extra layer of excitement to your viewing experience. Our expert betting predictions are designed to help you make informed decisions. Our analysts use advanced statistical models and deep insights into team form, player performance, and tactical setups to provide accurate predictions.
- Prediction Accuracy: Trust in our track record of successful predictions.
- Detailed Analysis: Understand the reasoning behind each prediction.
- Betting Tips: Receive tips on potential value bets and strategies.
In-Depth Team Profiles: Know Your Teams Inside Out
Understanding the teams is key to appreciating the nuances of Serie B Italy football. Our platform offers comprehensive profiles for each team, covering their history, key players, recent form, and tactical approaches.
- Team History: Explore the rich history and achievements of each club.
- Key Players: Learn about the standout performers who could make a difference.
- Tactical Insights: Gain insights into the strategies employed by different teams.
Player Spotlights: Meet the Stars of Tomorrow
Serie B Italy is a breeding ground for future football stars. Our player spotlights feature emerging talents who are making waves in the league. Discover their journey, skills, and potential impact on their respective teams.
- Rising Stars: Highlight promising young players to watch out for.
- Career Progression: Follow their development from youth academies to professional football.
- Performance Analysis: Detailed breakdowns of their playing style and strengths.
Match Previews: Get Ready for Each Game
Ahead of every matchday, our platform provides detailed previews to set the stage for what's to come. These previews cover all aspects of the upcoming fixtures, ensuring you're well-prepared for the action.
- Head-to-Head Records: Examine past encounters between the teams.
- Injury Updates: Stay informed about key player injuries and suspensions.
- Tactical Formations: Analyze expected line-ups and formations.
Live Commentary: Experience Matches Like Never Before
Our live commentary brings matches to life with real-time analysis and expert insights. Whether you're watching at home or on the go, our commentary team ensures you don't miss a moment of the action.
- Vivid Descriptions: Capture every moment with detailed commentary.
- Tactical Breakdowns: Understand key tactical shifts during the game.
- Possession Stats: Track possession percentages and other live stats.
The Thrill of Promotion and Relegation: The High Stakes of Serie B
The battle for promotion to Serie A is one of the most compelling aspects of Serie B Italy football. With only two spots available for promotion each season, every match carries immense significance. Conversely, relegation battles add an additional layer of drama as teams fight to avoid dropping into Serie C.
- Promotion Race: Follow the top teams vying for a spot in Serie A.
- Relegation Struggles: Witness the tense battles at the bottom of the table.
- Mid-Table Dynamics: Understand how mid-table positions can impact promotion/relegation hopes.
The Role of Fan Culture in Serie B Italy Football
Akshay-Mahajan/akshay-mahajan.github.io<|file_sep|>/_posts/2018-06-13-cats-vs-dogs.md
---
layout: post
title: Cats vs Dogs
tags: [deep learning]
---
**Abstract:** In this project I will be using [TensorFlow](https://www.tensorflow.org/) to build a deep neural network that can differentiate between cats and dogs from images.
### Introduction
In this project I will be using [TensorFlow](https://www.tensorflow.org/) to build a deep neural network that can differentiate between cats and dogs from images.
### Data
I am going to be using data from [Kaggle](https://www.kaggle.com/c/dogs-vs-cats/data) which contains 25k images split equally between cats & dogs.
![cats_vs_dogs.jpg]({{ "/assets/images/cats_vs_dogs.jpg" | absolute_url }})
The images were downloaded using this script.
python
import os
import requests
def download_images(image_names):
# Create directory if it doesn't exist
if not os.path.exists('images'):
os.makedirs('images')
# Download image
for image_name in image_names:
r = requests.get('https://raw.githubusercontent.com/dennybritz/deeplearning-pandas/master/images/' + image_name)
open('images/' + image_name.split('/')[-1], 'wb').write(r.content)
### Preprocessing
For this project I will be preprocessing images by doing:
* Resizing images (to speed up training)
* Converting them to grayscale (to reduce computational overhead)
The following function resizes images (to 100x100 pixels) & converts them to grayscale:
python
import cv2
def preprocess_image(file_path):
# Read image
img = cv2.imread(file_path)
# Resize image (to speed up training)
img = cv2.resize(img, (100,100))
# Convert image to grayscale (to reduce computational overhead)
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
return img
I then apply this function to all images:
python
from tqdm import tqdm
# Create list containing paths to all images
image_paths = []
for image_type in ['train', 'test']:
path = os.path.join(os.getcwd(), 'data', image_type)
for img in tqdm(os.listdir(path)):
if img.endswith('.jpg'):
image_paths.append(os.path.join(path,img))
# Apply preprocessing function to all images
images = []
for path in tqdm(image_paths):
# Preprocess image
img = preprocess_image(path)
# Append preprocessed image data (as flattened array) & label (1 if dog; else 0)
label = int(path.split('/')[-1].split('.')[0].split('_')[0] == 'dog')
images.append([img.flatten(), label])
After preprocessing I split my data into training & testing sets:
python
from sklearn.model_selection import train_test_split
X = np.array([i[0] for i in images]).astype(np.float32)
y = np.array([i[1] for i in images]).astype(np.uint8)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.25)
### Building Model
For this project I am going to use [TensorFlow](https://www.tensorflow.org/) which is an open source software library for numerical computation using data flow graphs.
![tensor_flow.jpg]({{ "/assets/images/tensor_flow.jpg" | absolute_url }})
In order words it allows you to create models using a high-level language like Python.
To get started we first import TensorFlow & set random seed:
python
import tensorflow as tf
tf.set_random_seed(42)
We then set some global variables:
python
learning_rate = .001
batch_size = 128
epochs = 5
num_steps = len(X_train) // batch_size
image_size = X_train.shape[1]
num_labels = len(set(y_train))
We then create placeholders for our input data (`x`) & labels (`y`):
python
x = tf.placeholder(tf.float32,[None,image_size])
y_ = tf.placeholder(tf.uint8,[None])
We then flatten our input data so it can be fed into a fully connected layer:
python
x_flat = tf.reshape(x,[batch_size,-1])
We then initialize weights & biases:
python
W_fc1 = tf.Variable(tf.truncated_normal([image_size,num_labels], stddev=.1), name="W_fc1")
b_fc1 = tf.Variable(tf.zeros([num_labels]), name="b_fc1")
We then define our model (using ReLU activation):
python
logits = tf.nn.relu(tf.matmul(x_flat,W_fc1)+b_fc1)
We then define our loss function (cross entropy):
python
loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=y_))
We then define our optimizer:
python
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
We then initialize variables:
python
init_op = tf.global_variables_initializer()
We then define an accuracy metric:
python
correct_prediction = tf.equal(tf.argmax(logits,1), y_)
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
Finally we run TensorFlow session & train model:
python
with tf.Session() as sess:
# Run initializer op.
sess.run(init_op)
# Train model
for step in range(epochs * num_steps):
batch_x,batch_y=next_batch(batch_size)
sess.run(train_op,
feed_dict={x: batch_x,
y_: batch_y})
if step % num_steps ==0:
loss=sess.run(loss_op,
feed_dict={x:X_test,
y_:y_test})
print("Epoch:",step//num_steps,
"Loss:",loss)
# Test model
pred=sess.run(logits,
feed_dict={x:X_test})
accuracy=sess.run(accuracy_op,
feed_dict={logits:pred,
y_:y_test})
print("Test Accuracy:",accuracy)
My final accuracy was `84%` which isn't too bad but definitely could be improved by adding more layers.
### References
* [Kaggle](https://www.kaggle.com/c/dogs-vs-cats/data) - Dogs vs Cats dataset.
* [Denny Britz - Deep Learning with Python](https://github.com/dennybritz/deeplearning-pandas) - Repository containing code used in Denny Britz' Udemy course.<|repo_name|>Akshay-Mahajan/akshay-mahajan.github.io<|file_sep|>/_posts/2019-08-04-scikit-learn-intro.md
---
layout: post
title: Scikit-Learn Introductory Tutorial
tags: [machine learning]
---
**Abstract:** This is my first machine learning project where I will build several machine learning models using scikit-learn & compare their performance.
### Introduction
This is my first machine learning project where I will build several machine learning models using scikit-learn & compare their performance.
### Data
I will be using Titanic data from [Kaggle](https://www.kaggle.com/c/titanic/data).
### Data Preparation
First let's import some libraries we will need throughout this tutorial:
python3
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import fbeta_score
from sklearn.metrics import make_scorer
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
import warnings
warnings.filterwarnings('ignore')
sns.set(style='white', context='notebook', palette='deep')
Now let's load dataset into Pandas DataFrame:
python3
df_train=pd.read_csv('../input/train.csv')
df_test=pd.read_csv('../input/test.csv')
df_combined=pd.concat([df_train,test])
Now let's take a look at dataset:
python3
df_train.head()
![image.png]({{ "/assets/images/scikit_learn_intro_1.png" | absolute_url }})
Next let's see how many rows & columns we have:
python3
print('Rows:',df_train.shape[0])
print('Columns:',df_train.shape[1])
print('nColumn Names:',df_train.columns.tolist())
![image.png]({{ "/assets/images/scikit_learn_intro_2.png" | absolute_url }})
Now let's see how many null values we have per column:
python3
df_train.isnull().sum()
![image.png]({{ "/assets/images/scikit_learn_intro_3.png" | absolute_url }})
Next let's see what percentage values are missing per column:
python3
round(100*(df_train.isnull().sum()/len(df_train.index)), 2).sort_values(ascending=False)[:5]
![image.png]({{ "/assets/images/scikit_learn_intro_4.png" | absolute_url }})
Now let's see what percentage people survived vs died:
python3
survived=df_train['Survived'].value_counts(normalize=True).sort_values(ascending=False)*100
sns.barplot(survived.index,survived.values,alpha=0.8)
plt.ylabel('%')
plt.title('Survival Rate')
plt.show()
![image.png]({{ "/assets/images/scikit_learn_intro_5.png" | absolute_url }})
Next let's see how many men vs women were on board:
python3
sex=df_train['Sex'].value_counts(normalize=True).sort_values(ascending=False)*100
sns.barplot(sex.index,sex.values,alpha=0.8)
plt.ylabel('%')
plt.title('Male vs Female ratio')
plt.show()
![image.png]({{ "/assets/images/scikit_learn_intro_6.png" | absolute_url }})
Now let's see how many people survived based on gender:
python3
survived=df_train.groupby(['Sex'])['Survived'].mean().reset_index()
survived=survived.sort_values(by='Survived',ascending=False).set_index('Sex')
sns.barplot(survived.index,survived['Survived'],alpha=0.8)
plt.ylabel('% survival rate')
plt.title('Survival rate by Gender')
plt.show()
![image.png]({{ "/assets/images/scikit_learn_intro_7.png" | absolute_url }})
Next let's see how many people survived based on class they were travelling in:
python3
survived=df_train.groupby(['Pclass'])['Survived'].mean().reset_index()
survived=survived.sort_values(by='Survived',ascending=False).set_index('Pclass')
sns.barplot(survived.index,survived['Survived'],alpha=0.8)
plt.ylabel('% survival rate')
plt.title('Survival rate by Pclass')
plt.show()
![image.png]({{ "/assets/images/scikit_learn_intro_8.png" | absolute_url }})
Next let's see how many people survived based on age they were travelling with kids or not:
python3
fig=plt.figure(figsize=(15,5))
ax=fig.add_subplot(121)
sns.kdeplot(df_train.loc[(df_train.Survived==1)&(df_train.Age<18),'Age'],shade=True,color='b',label='Survived')
sns.kdeplot(df_train.loc[(df_train.Survived==0)&(df_train.Age<18),'Age'],shade=True,color='r',label='Died')
ax.legend()
ax.set_xlabel('Age')
ax.set_ylabel('% Count')
ax.set_title('% Distribution by Age & Survival Status')
ax=fig.add_subplot(122)
sns.kdeplot(df_train.loc[(df_train.Survived==