Project Name: Car price prediction Kaggle Dataset: https://www.kaggle.com/hellbuoy/car-price-prediction General Description: For understanding pricing dynamics of the new market in the different cars for business growth, we will predict the car’s prices depending on different independent variables....
car_price_np = np.array(car_prices_array, dtype = np.float32) car_price_np = car_price_np.reshape(-1, 1) car_price_tensor = Variable(torch.from_numpy(car_price_np)) # 车销量 number_of_car_sell_array = [7.5, 7, 6.5, 6.0, 5.5, 5.0, 4.5] number_of_car_sell_np = np.arra...
This contains an Artificial Neural Network (ANN) model trained to predict second-hand car prices. Leveraging supervised learning techniques and advanced data preprocessing, this model offers accurate price predictions based on a dataset sourced from Kagg
The Boston Housing dataset is another popular dataset on Kaggle. This dataset contains information about housing in the city of Boston. It has over 200,000 records and 18 variables. The goal of this dataset is to predict whether or not a house price is expensive. The dataset...
("/kaggle/input/llm-dataset/gen_llm_fac_v1.csv") #lm_ali_2 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_elec_v1.csv") #lm_ali_3 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_car_free_v1.csv") lm_ali_4 = pd.read_csv("/kaggle/input/llm-dataset/gen_llm_...
sns.set_style("white")sns.set_color_codes(palette='deep')f,ax=plt.subplots(figsize=(8,7))#Check thenewdistributionsns.distplot(train['SalePrice'],color="b");ax.xaxis.grid(False)ax.set(ylabel="Frequency")ax.set(xlabel="SalePrice")ax.set(title="SalePrice distribution")sns.despine(tri...
FLAG_OWN_CAR,客户是否有车 FLAG_OWN_REALTY,客户是否有不动产 CNT_CHILDREN,客户有几个孩子 AMT_INCOME_TOTAL,客户收入 AMT_CREDIT,贷款金额 AMT_ANNUITY,贷款年金 AMT_GOODS_PRICE,消费贷款金额=消费金额 NAME_TYPE_SUITE,客户在申请贷款是陪同人员情况 ...
SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict. MSSubClass: The building class MSZoning: The general zoning classification LotFrontage: Linear feet of street connected to property LotArea: Lot size in square feet Street: Type...
Your dataset had too many variables to wrap your head around(to accept something that one does not particularly want to accept), or even to print out nicely. How can you pare down(减少) this overwhelming amount of data to something you can understand?
NAME_CONTRACT_TYPECODE_GENDERFLAG_OWN_CARFLAG_OWN_REALTYCNT_CHILDRENAMT_INCOME_TOTALAMT_CREDITAMT_ANNUITYAMT_GOODS_PRICENAME_TYPE_SUITE...FLAG_DOCUMENT_18FLAG_DOCUMENT_19FLAG_DOCUMENT_20FLAG_DOCUMENT_21AMT_REQ_CREDIT_BUREAU_HOURAMT_REQ_CREDIT_BUREAU_DAYAMT_REQ_...