Overview of Tomorrow's Erovnuli Liga Georgia Matches
The Georgian Premier League, known as Erovnuli Liga, is set to host an exciting round of matches tomorrow. Football fans and bettors alike are eagerly anticipating the thrilling encounters that will unfold on the pitch. With teams vying for supremacy, each match promises to be a tactical battle filled with opportunities for both surprise upsets and expected victories. This article provides expert betting predictions and insights into the key matches scheduled for tomorrow, offering valuable information for enthusiasts looking to engage with the league's latest developments.
Key Matches and Expert Predictions
Dinamo Tbilisi vs. Samtredia
Dinamo Tbilisi, one of the most successful clubs in Georgian football history, faces off against Samtredia in what is expected to be a tightly contested match. Dinamo's strong home record and experienced squad make them favorites to secure a victory. However, Samtredia's resilience and tactical discipline under their current manager suggest they could pose a significant challenge.
- Betting Prediction: Dinamo Tbilisi to win 2-1
- Key Players: Giorgi Kvilitaia (Dinamo Tbilisi) and Davit Skhirtladze (Samtredia)
Sioni Bolnisi vs. Saburtalo Tbilisi
Sioni Bolnisi, known for their solid defensive performances, will host Saburtalo Tbilisi. Saburtalo's attacking prowess, led by their star striker, makes them a formidable opponent. This match is anticipated to be a clash between Sioni's defensive strategy and Saburtalo's offensive tactics.
- Betting Prediction: Draw (1-1)
- Key Players: Nikoloz Gelashvili (Sioni Bolnisi) and Mamuka Kobakhidze (Saburtalo Tbilisi)
Lokomotivi Tbilisi vs. Dila Gori
Lokomotivi Tbilisi aims to maintain their position at the top of the table when they face Dila Gori. Lokomotivi's recent form has been impressive, with consistent performances across all competitions. Dila Gori, on the other hand, will look to disrupt Lokomotivi's rhythm and secure vital points.
- Betting Prediction: Lokomotivi Tbilisi to win 3-0
- Key Players: Shota Grigalava (Lokomotivi Tbilisi) and Gogita Gogua (Dila Gori)
Guria Lanchkhuti vs. Torpedo Kutaisi
Guria Lanchkhuti hosts Torpedo Kutaisi in a match that could determine their respective positions in the league standings. Both teams have shown improvement this season, making this encounter unpredictable. Guria's home advantage could be crucial in tipping the scales in their favor.
- Betting Prediction: Guria Lanchkhuti to win 1-0
- Key Players: Guram Papava (Guria Lanchkhuti) and Zviad Endeladze (Torpedo Kutaisi)
In-Depth Match Analysis
Dinamo Tbilisi vs. Samtredia: Tactical Breakdown
Dinamo Tbilisi enters this match with high expectations from their fans. Their tactical setup often revolves around a strong midfield presence, allowing them to control the tempo of the game. Coach Temur Ketsbaia is known for his strategic acumen, frequently adjusting his lineup to exploit opponents' weaknesses.
Samtredia, under the guidance of Kakhaber Tskhadadze, has shown remarkable defensive solidity. Their ability to absorb pressure and counterattack swiftly makes them a tough nut to crack. The key to this match will be Dinamo's ability to break down Samtredia's organized defense without leaving too much space for counterattacks.
- Dinamo's Strengths: Midfield dominance, experienced squad
- Samtredia's Strengths: Defensive organization, counterattacking threat
Sioni Bolnisi vs. Saburtalo Tbilisi: A Clash of Styles
This match presents a classic clash of styles: Sioni Bolnisi's defensive resilience against Saburtalo Tbilisi's attacking flair. Sioni Bolnisi has been lauded for their ability to frustrate opponents with a well-drilled backline and disciplined midfielders who cut off passing lanes effectively.
Saburtalo Tbilisi, however, boasts some of the league's most talented young forwards who thrive on creating scoring opportunities out of seemingly nothing. Their fluid attacking play can unsettle even the most organized defenses.
- Sioni Bolnisi's Strategy: Maintain shape, hit on the break
- Saburtalo Tbilisi's Strategy: Press high, exploit spaces behind the defense
Lokomotivi Tbilisi vs. Dila Gori: A Battle for Top Spot
Lokomotivi Tbilisi has been in excellent form recently, showcasing their depth and quality across all areas of the pitch. Their balanced approach allows them to transition smoothly between defense and attack, making them difficult to predict.
Dila Gori will need to be at their best defensively while finding moments to capitalize on any lapses in Lokomotivi's concentration. The pressure will be on Lokomotivi to deliver a performance worthy of their status as league leaders.
- Lokomotivi Tbilisi's Key Asset: Versatile squad capable of adapting to different game situations
- Dila Gori's Key Asset: Resilience and ability to perform under pressure
Guria Lanchkhuti vs. Torpedo Kutaisi: An Unpredictable Encounter
danishsharif/kaggle<|file_sep|>/house-prices-advanced-regression-techniques/feature_analysis.py
import numpy as np
import pandas as pd
from sklearn import ensemble
from sklearn.model_selection import cross_val_score
data_train = pd.read_csv("data/train.csv")
data_test = pd.read_csv("data/test.csv")
# Set target variable
target = "SalePrice"
y_train = data_train[target]
X_train = data_train.drop(target,axis=1)
X_test = data_test
# Drop variables that do not contribute much
X_train.drop(["Id","Alley","PoolQC","Fence","MiscFeature"],axis=1,inplace=True)
X_test.drop(["Id","Alley","PoolQC","Fence","MiscFeature"],axis=1,inplace=True)
# Remove outliers
# X_train.drop(X_train[X_train["GrLivArea"] >4000].index,inplace=True)
# y_train.drop(y_train[y_train["GrLivArea"] >4000].index,inplace=True)
# Fill missing values
X_train["LotFrontage"] = X_train["LotFrontage"].fillna(X_train["LotFrontage"].median())
X_test["LotFrontage"] = X_test["LotFrontage"].fillna(X_test["LotFrontage"].median())
for col in ("GarageType", "GarageFinish", "GarageQual", "GarageCond"):
X_train[col] = X_train[col].fillna('None')
X_test[col] = X_test[col].fillna('None')
for col in ("GarageYrBlt", "GarageArea", "GarageCars"):
X_train[col] = X_train[col].fillna(0)
X_test[col] = X_test[col].fillna(0)
for col in ("BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF","TotalBsmtSF", "BsmtFullBath", "BsmtHalfBath"):
X_train[col] = X_train[col].fillna(0)
X_test[col] = X_test[col].fillna(0)
for col in ("BsmtQual", "BsmtCond", "BsmtExposure", "BsmtFinType1", "BsmtFinType2"):
X_train[col] = X_train[col].fillna('None')
X_test[col] = X_test[col].fillna('None')
X_train["MasVnrType"] = X_train["MasVnrType"].fillna('None')
X_test["MasVnrType"] = X_test["MasVnrType"].fillna('None')
X_train["MasVnrArea"] = X_train["MasVnrArea"].fillna(0)
X_test["MasVnrArea"] = X_test["MasVnrArea"].fillna(0)
X_train['MSZoning'] = X_train['MSZoning'].fillna(X_train['MSZoning'].mode()[0])
X_test['MSZoning'] = X_test['MSZoning'].fillna(X_test['MSZoning'].mode()[0])
for col in ('FireplaceQu', 'KitchenQual', 'Exterior1st', 'Exterior2nd', 'SaleType'):
X_train[col] = X_train[col].fillna(X_train[col].mode()[0])
X_test[col] = X_test[col].fillna(X_test[col].mode()[0])
X_train['Functional'] = X_train['Functional'].fillna('Typ')
X_test['Functional'] = X_test['Functional'].fillna('Typ')
for col in ('MSSubClass', 'OverallCond', 'YrSold', 'MoSold'):
X_train[col] = X_train[col].astype(str)
X_test[col] = X_test[col].astype(str)
# Transform categorical variables into dummies
object_cols = [col for col in list(X_train) if str(X_train.dtypes.loc[col]) == 'object']
for col in object_cols:
cat_list='var'+'_'+col
cat_list=pd.get_dummies(X_train[col],prefix=col)
# data1=X_train.join(cat_list)
# data1.drop([col], axis=1,inplace=True)
data2=X_test.join(cat_list)
data2.drop([col], axis=1,inplace=True)
# print(data1.shape)
# if col == object_cols[0]:
# df_dummies=data1
# else:
# df_dummies=pd.concat([df_dummies,data1],axis=1)
# df_dummies.drop([col], axis=1,inplace=True)
# print(df_dummies.shape)
if object_cols:
df_dummies=data2
else:
df_dummies=X_test
print(df_dummies.shape)
object_cols_t=[col for col in list(X_train) if str(X_train.dtypes.loc[col]) == 'object']
for col in object_cols_t:
if col not in ['Street','Utilities','Condition2','RoofMatl']:
#print(col)
cat_list='var'+'_'+col
cat_list=pd.get_dummies(X_train[col],prefix=col)
#print(cat_list.shape[1])
data1=X_train.join(cat_list)
data1.drop([col], axis=1,inplace=True)
if col == object_cols_t[0]:
df_dummies_t=data1
else:
df_dummies_t=pd.concat([df_dummies_t,data1],axis=1)
df_dummies_t.drop([col], axis=1,inplace=True)
print(df_dummies_t.shape)
object_cols_tt=[col for col in list(X_test) if str(X_test.dtypes.loc[col]) == 'object']
for col in object_cols_tt:
if col not in ['Street','Utilities','Condition2','RoofMatl']:
#print(col)
cat_list='var'+'_'+col
cat_list=pd.get_dummies(X_test[col],prefix=col)
#print(cat_list.shape[1])
data2=X_test.join(cat_list)
data2.drop([col], axis=1,inplace=True)
if col == object_cols_tt[0]:
df_dummies_tt=data2
else:
df_dummies_tt=pd.concat([df_dummies_tt,data2],axis=1)
df_dummies_tt.drop([col], axis=1,inplace=True)
print(df_dummies_tt.shape)
final_df=pd.merge(df_dummies,df_dummies_t,on=df_dummies.index,right_index=True,suffixes=('','_drop'))
final_df.columns=[x.replace('_drop','') for x in final_df.columns]
final_df.set_index(df_dummies.index,inplace=True)
final_df.drop([x for x in final_df.columns if '_drop' in x],axis=1,inplace=True)
final_df_tt=pd.merge(df_dummies_tt,df_dummies_tt,on=df_dummies_tt.index,right_index=True,suffixes=('','_drop'))
final_df_tt.columns=[x.replace('_drop','') for x in final_df_tt.columns]
final_df_tt.set_index(df_dummies_tt.index,inplace=True)
final_df_tt.drop([x for x in final_df_tt.columns if '_drop' in x],axis=1,inplace=True)
y=np.log(y_train)
features=['LotArea',
'YearBuilt',
'YearRemodAdd',
'MasVnrArea',
'BsmtFinSF1',
'BsmtFinSF2',
'TotalBsmtSF',
'FirstFlrSF',
'SecondFlrSF',
'LowQualFinSF',
'GrLivArea',
'BsmtFullBath',
'FullBath',
'HalfBath',
'BedroomAbvGr',
'KitchenAbvGr',
'TotRmsAbvGrd',
'Fireplaces',
'GarageCars',
'WoodDeckSF',
'OpenPorchSF']
X=np.array(final_df[features])
features_t=['LotArea',
'YearBuilt',
'YearRemodAdd',
'MasVnrArea',
'BsmtFinSF1',
'BsmtFinSF2',
'TotalBsmtSF',
#'FirstFlrSF'
#'SecondFlrSF'
#'LowQualFinSF'
#'GrLivArea'
#'FullBath'
#'HalfBath'
#'BedroomAbvGr'
#'KitchenAbvGr'
#'TotRmsAbvGrd'
#'Fireplaces'
#'GarageCars'
#'WoodDeckSF'
]
X_t=np.array(final_df_t[features_t])
features_tt=['LotArea',
#'YearBuilt'
#'YearRemodAdd'
#'MasVnrArea'
#'BsmtFinSF1'
#'BsmtFinSF2'
#'TotalBsmtSF'
#'FirstFlrSF'
#'SecondFlrSF'
#'LowQualFinSF'
#'GrLivArea'
#'FullBath'
#'HalfBath'
#'BedroomAbvGr'
#'KitchenAbvGr'
#'TotRmsAbvGrd'
#'Fireplaces'
#'GarageCars'
]
X_tt=np.array(final_df_tt[features_tt])
model_rfr=ensemble.RandomForestRegressor(n_estimators=100,oob_score=False,n_jobs=-1,max_depth=None,max_features='auto')
model_rfr.fit(X,y)
scores=cross_val_score(model_rfr,X,y,cv=5,n_jobs=-1)
print("Mean Accuracy:",scores.mean())
print("Accuracy Std:",scores.std())
preds=model_rfr.predict(X_t)
preds=preds.reshape(-1,)
preds=np.exp(preds)-1
submission=pd.DataFrame({"Id":data_test.Id,"SalePrice":preds})
submission.to_csv("feature_analysis.csv",index=False)<|repo_name|>danishsharif/kaggle<|file_sep|>/house-prices-advanced-regression-techniques/feature_selection.py
import numpy as np
import pandas as pd
from sklearn import ensemble
from sklearn.model_selection import cross_val_score
data_frame=pd.read_csv("data/train.csv")
target="SalePrice"
y=data_frame[target]
data_frame=data_frame.drop(target,axis=1)
data_frame.drop(["Id","Alley","PoolQC","Fence","MiscFeature"],axis=1,inplace=True)
data_frame=data_frame[data_frame.GrLivArea<4000]
data_frame.LotFrontage=data_frame.LotFrontage.fillna(data_frame.LotFrontage.median())
for col in ("GarageType", "GarageFinish", "GarageQual", "GarageCond"):
data_frame[col]=data_frame[col].fillna('None')
for col in ("GarageYrBlt", "GarageArea", "GarageCars"):
data_frame[col]=data_frame[col].fillna(0)
for col in ("BsmtFinSF1", "BsmtFinSF2", "BsmtUnfSF","TotalBsmtSF", "BsmtFullBath", "BsmtHalfBath"):
data_frame[col]=data_frame[col].fillna(0)
for col in ("BsmtQual", "BsmtCond", "BsmtExposure", "BsmtFinType1", "BsmtFinType2"):
data_frame[col]=data_frame[col].fillna('None')
data_frame.MasVnrType=data_frame.MasVnrType.fillna('None')
data_frame.MasVnrArea=data_frame.MasVnrArea.fillna(0)
data_frame.MSZoning=data_frame.MSZoning.fillna(data_frame.MSZoning.mode()[0])
for col in ('FireplaceQu', 'KitchenQual', 'Exterior1st', 'Exterior2nd