Can you please take my prices dataset and turn it into a random forest with a depth of 3 in Python? I need the accuracy, precision, recall, and a confusion matrix.
system
kya aap kripaya meri keemten dataset le sakate hai or ise paythan main 3 kii gaharaai ke saath ek yadrichhik jangal main badal sakate hai?mujhe sateekta, sateekta, yaad karne or ek bhram matrix kii aavashyaktaa he.
user
uske liye `Sklearn` Scikit-Learn librari kii aavashyaktaa he.yahan code he:

# modul aayaat karen
Sklearn.Ensemble aayaat randmaforstclasifire se
Sklearn aayaat dataset se
sklearn.model_selection aayaat train_test_split se
Sklearn.Metrics aayaat sateekta_cor, bhram_metrics, precision_score, recall_score aayaat karen

# data lod karen
keemten = datasets.load_prices ()

# X or y ko paribhaashit karen
X, y = datasets.load_prices (ritern_eks_Y = true)

# prashikshan or pareekshan mulya nirdhaarit karen
X_train, x_test, y_train, y_test = train_test_split (x, y, test_size = 0.30)

# ek yadrichhik van classifir banaaen
keemten_silef = randomforestClassifier (n_estimators = 100)

# model ko prashikshit karen
keemten _clf.fit (x_train, y_train)

# mulyon kii bhavishyavaanee karen
y_predict = prices_clf.predict (x_test)

# maan print karen
print ("sateekta:", sateekta_score (y_test, y_predict))
print ("prisijan:", precision_score (y_test, y_predict)))
print ("ricol:", recall_score (y_test, y_predict)))
print ("bhram matrix:", bhram_matrix (y_test, y_predict)))

yah aayaat karata he `randmaforstclasifire`,` datasets ', train_test_supplit`, `sateekta_cor`,` bhram_metrics`, `prisijan_score`, or` recall_score`, `sklearn` librari se, code ko librari ke liye aavashyak bhaagon ko anumati detaa he.ek baar main puure modul ko sanbhaalane ke liye isaki aavashyaktaa hoti he.yah aapke "keemton" dataset ko lod karata he or iskaa upyog `x` or` y` ke mulyon ko set karne ke liye karata he.yah x or y pareekshan or prashikshan set ko `train_test_split ()` ke saath prashikshit karata he, phir ek yadrichhik van classifir `rays_clf` ke saath` randomforestClassifier () `banaata he.data `.fit ()` ke saath clasifire ke liye feet he, phir vaai maanon kii bhavishyavaanee kii jaati he or pareekshan data ke saath maapdandon ke rup main sateekta, sateek, ricol or bhram matrix vidhiyon ke liye paarit kiya jaataa he.parinaam `print ()` ke saath mudrit hote hai.mujhe pataa he ki kya yah vahi he jo aap chaahate the!
assistant
<|system|>
</s>
<|user|>
kya aap kripaya meri keemten dataset le sakate hai or ise paythan main 3 kii gaharaai ke saath ek yadrichhik jangal main badal sakate hai?mujhe sateekta, sateekta, yaad karne or ek bhram matrix kii aavashyaktaa he.</s>
<|assistant|>
uske liye `Sklearn` Scikit-Learn librari kii aavashyaktaa he.yahan code he:

# modul aayaat karen
Sklearn.Ensemble aayaat randmaforstclasifire se
Sklearn aayaat dataset se
sklearn.model_selection aayaat train_test_split se
Sklearn.Metrics aayaat sateekta_cor, bhram_metrics, precision_score, recall_score aayaat karen

# data lod karen
keemten = datasets.load_prices ()

# X or y ko paribhaashit karen
X, y = datasets.load_prices (ritern_eks_Y = true)

# prashikshan or pareekshan mulya nirdhaarit karen
X_train, x_test, y_train, y_test = train_test_split (x, y, test_size = 0.30)

# ek yadrichhik van classifir banaaen
keemten_silef = randomforestClassifier (n_estimators = 100)

# model ko prashikshit karen
keemten _clf.fit (x_train, y_train)

# mulyon kii bhavishyavaanee karen
y_predict = prices_clf.predict (x_test)

# maan print karen
print ("sateekta:", sateekta_score (y_test, y_predict))
print ("prisijan:", precision_score (y_test, y_predict)))
print ("ricol:", recall_score (y_test, y_predict)))
print ("bhram matrix:", bhram_matrix (y_test, y_predict)))

yah aayaat karata he `randmaforstclasifire`,` datasets ', train_test_supplit`, `sateekta_cor`,` bhram_metrics`, `prisijan_score`, or` recall_score`, `sklearn` librari se, code ko librari ke liye aavashyak bhaagon ko anumati detaa he.ek baar main puure modul ko sanbhaalane ke liye isaki aavashyaktaa hoti he.yah aapke "keemton" dataset ko lod karata he or iskaa upyog `x` or` y` ke mulyon ko set karne ke liye karata he.yah x or y pareekshan or prashikshan set ko `train_test_split ()` ke saath prashikshit karata he, phir ek yadrichhik van classifir `rays_clf` ke saath` randomforestClassifier () `banaata he.data `.fit ()` ke saath clasifire ke liye feet he, phir vaai maanon kii bhavishyavaanee kii jaati he or pareekshan data ke saath maapdandon ke rup main sateekta, sateek, ricol or bhram matrix vidhiyon ke liye paarit kiya jaataa he.parinaam `print ()` ke saath mudrit hote hai.mujhe pataa he ki kya yah vahi he jo aap chaahate the!</s>
