<jupyter_start><jupyter_text>Data Description1. count shows rows with some values.2. mean is the average.3. std is the standard deviation.4. min is the minimum and max is the maximum. 25%-75% shows us percentile score based values<jupyter_code># calling average value od our dataset
melbourne_data.mean()
# print all the columns
melbourne_data.columns
# dropring not available in empty areas
melbourne_data = melbourne_data.dropna()
# features dataset
y = melbourne_data.Price
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude'] 
X = melbourne_data[melbourne_features]
# sklearn model
melbourne_model = DecisionTreeRegressor(random_state=1)
melbourne_model.fit(X, y)
# prediction
print("predicting price for these 5 values :")
print(X.head())
print("are : ")
print(melbourne_model.predict(X.head()))<jupyter_output>predicting price for these 5 values :
   Rooms  Bathroom  Landsize  Lattitude  Longtitude
1      2       1.0     156.0   -37.8079    144.9934
2      3       2.0     134.0   -37.8093    144.9944
4      4       1.0     120.0   -37.8072    144.9941
6      3       2.0     245.0   -37.8024    144.9993
7      2       1.0     256.0   -37.8060    144.9954
are : 
[1035000. 1465000. 1600000. 1876000. 1636000.]
{'fraction_non_alphanumeric': 0.09615384615384616, 'fraction_numerical': 0.12099358974358974, 'mean_word_length': 3.1912751677852347, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '384193', 'license': 'MIT', 'n_tokens_mistral': 509, 'n_tokens_neox': 430, 'n_words': 127, 'path': 'Machine learning/kaggle ml course/kaggle.ipynb', 'repo_name': 'deepaklorkhatri007/AI', 'size': '1248'}